{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别与计算机视觉文档实践\n",
    "\n",
    ">* 本周主要内容：人脸（Face） API文档不同平台对比与实践及计算机视觉入门（认知服务）\n",
    ">* 202009_API_人工智能与机器学习_week03\n",
    ">*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 复习\n",
    "\n",
    "* 上周主要内容： \n",
    ">    * 1、API文档\n",
    ">    * 2、认知服务-人脸识别\n",
    ">    * 3、pandas黑魔法（json_normalize）\n",
    "-----\n",
    "* 提问、检查与扩展：\n",
    ">    * 1、上周的Azure face API随机检查2位同学，对于API文档是否有能力详细阅读？\n",
    ">    * 2、抽查2位同学，对于获取的数据用途有何思考？是否尝试其他平台（XXX API、XXX API）      \n",
    ">    * 3、扩展: 我们尝试抽取了4张图片，包含AB、ABC、BCD、ABCD四个人物特征，如何通过API检查数据差异？\n",
    "  \n",
    "\n",
    "-----\n",
    "## 本周学习目标：\n",
    "\n",
    "> $\\mathcal{1、Azure 认知服务-人脸集合演示}$     \n",
    "> $\\mathcal{2、Face++ 之 FaceSets 实践}$     \n",
    "> $\\mathcal{3、计算机视觉实践}$    \n",
    "\n",
    "\n",
    "\n",
    "## 学生权限\n",
    "\n",
    "* [访问学生权益](https://azure.microsoft.com/zh-cn/education/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #e5f1fe;\n",
       "}\n",
       "div.cell.selected {\n",
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       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 50%;\n",
       "    height: auto;\n",
       "    float: center;\n",
       "}\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "\n",
       "</style>\n"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
    "}\n",
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    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
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    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 50%;\n",
    "    height: auto;\n",
    "    float: center;\n",
    "}\n",
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    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复习\n",
    "\n",
    "> 1、回顾阅读API文档的关键点     \n",
    "> 2、正确阅读json数据 [jsonviewer.stack.hu(json转换检查数据)](http://jsonviewer.stack.hu)    \n",
    "> 3、pandas 中的json_normalize模块/函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"c754f28f-ee36-4153-bc04-5b7c067edee3\", \"faceRectangle\": {\"top\": 182, \"left\": 117, \"width\": 178, \"height\": 178}, \"faceAttributes\": {\"smile\": 0.99, \"headPose\": {\"pitch\": -14.4, \"roll\": -1.0, \"yaw\": -14.4}, \"gender\": \"female\", \"age\": 21.0, \"facialHair\": {\"moustache\": 0.0, \"beard\": 0.0, \"sideburns\": 0.0}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.001, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.99, \"neutral\": 0.009, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"medium\", \"value\": 0.26}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.71}, \"noise\": {\"noiseLevel\": \"medium\", \"value\": 0.37}, \"makeup\": {\"eyeMakeup\": true, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.08, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 0.99}, {\"color\": \"brown\", \"confidence\": 0.9}, {\"color\": \"gray\", \"confidence\": 0.34}, {\"color\": \"other\", \"confidence\": 0.27}, {\"color\": \"blond\", \"confidence\": 0.1}, {\"color\": \"red\", \"confidence\": 0.03}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"9415313ee78e4d79a0d6cf603d0177f0\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 json转译\n",
    "\n",
    "> * bytes ---> json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '8da55c89-3cc5-4b58-abec-34cdc2e0f5c8',\n",
       "  'faceRectangle': {'top': 182, 'left': 117, 'width': 178, 'height': 178},\n",
       "  'faceAttributes': {'smile': 0.99,\n",
       "   'headPose': {'pitch': -14.4, 'roll': -1.0, 'yaw': -14.4},\n",
       "   'gender': 'female',\n",
       "   'age': 21.0,\n",
       "   'facialHair': {'moustache': 0.0, 'beard': 0.0, 'sideburns': 0.0},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.001,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.99,\n",
       "    'neutral': 0.009,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'medium', 'value': 0.26},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.71},\n",
       "   'noise': {'noiseLevel': 'medium', 'value': 0.37},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.08,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 0.99},\n",
       "     {'color': 'brown', 'confidence': 0.9},\n",
       "     {'color': 'gray', 'confidence': 0.34},\n",
       "     {'color': 'other', 'confidence': 0.27},\n",
       "     {'color': 'blond', 'confidence': 0.1},\n",
       "     {'color': 'red', 'confidence': 0.03},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1f2bf379-70dd-41be-86c3-a63f2deb7c7f</td>\n",
       "      <td>118</td>\n",
       "      <td>144</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>0.813</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.18</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'brown', 'confidence': 0.95}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f659ea05-a658-497d-9b2a-eb89e74d5403</td>\n",
       "      <td>117</td>\n",
       "      <td>376</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0.456</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5e17a0f5-0a18-4a83-821a-31dd3fd014cd</td>\n",
       "      <td>41</td>\n",
       "      <td>676</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c9b6de20-4b5c-4a87-88ce-482b2049a45c</td>\n",
       "      <td>69</td>\n",
       "      <td>445</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c29e7a06-72de-4b77-bc5d-5568145f10eb</td>\n",
       "      <td>95</td>\n",
       "      <td>238</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>0.981</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'type': 'glasses', 'confidence': 1.0}]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 0.98}, {'col...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0192c19c-eb0a-447b-82f1-740f32eb004a</td>\n",
       "      <td>94</td>\n",
       "      <td>540</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>female</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  1f2bf379-70dd-41be-86c3-a63f2deb7c7f                118   \n",
       "1  f659ea05-a658-497d-9b2a-eb89e74d5403                117   \n",
       "2  5e17a0f5-0a18-4a83-821a-31dd3fd014cd                 41   \n",
       "3  c9b6de20-4b5c-4a87-88ce-482b2049a45c                 69   \n",
       "4  c29e7a06-72de-4b77-bc5d-5568145f10eb                 95   \n",
       "5  0192c19c-eb0a-447b-82f1-740f32eb004a                 94   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                 144                   88                    88   \n",
       "1                 376                   64                    64   \n",
       "2                 676                   52                    52   \n",
       "3                 445                   52                    52   \n",
       "4                 238                   51                    51   \n",
       "5                 540                   48                    48   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                 0.813                           -3.4   \n",
       "1                 0.456                           -1.1   \n",
       "2                 1.000                            3.0   \n",
       "3                 1.000                            0.2   \n",
       "4                 0.981                            7.2   \n",
       "5                 1.000                           -7.7   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                           1.4                          9.4   \n",
       "1                          -0.2                          6.6   \n",
       "2                          -1.3                         -0.6   \n",
       "3                           2.6                          1.5   \n",
       "4                           1.2                          2.6   \n",
       "5                           5.0                          6.9   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                  male  ...                        0.01   \n",
       "1                female  ...                        0.01   \n",
       "2                  male  ...                        0.07   \n",
       "3                female  ...                        0.33   \n",
       "4                female  ...                        0.48   \n",
       "5                female  ...                        0.08   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                            False                            False   \n",
       "1                            False                             True   \n",
       "2                            False                            False   \n",
       "3                            False                            False   \n",
       "4                            False                            False   \n",
       "5                            False                            False   \n",
       "\n",
       "                 faceAttributes.accessories  \\\n",
       "0                                        []   \n",
       "1                                        []   \n",
       "2  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "3                                        []   \n",
       "4  [{'type': 'glasses', 'confidence': 1.0}]   \n",
       "5                                        []   \n",
       "\n",
       "  faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                                     False   \n",
       "1                                     False   \n",
       "2                                     False   \n",
       "3                                     False   \n",
       "4                                     False   \n",
       "5                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "1                                 False   \n",
       "2                                 False   \n",
       "3                                 False   \n",
       "4                                 False   \n",
       "5                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.18   \n",
       "1                                   False                      0.08   \n",
       "2                                   False                      0.06   \n",
       "3                                   False                      0.11   \n",
       "4                                   False                      0.11   \n",
       "5                                   False                      0.10   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "1                          False   \n",
       "2                          False   \n",
       "3                          False   \n",
       "4                          False   \n",
       "5                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'brown', 'confidence': 0.95}, {'col...  \n",
       "1  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "2  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "3  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "4  [{'color': 'black', 'confidence': 0.98}, {'col...  \n",
       "5  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "\n",
       "[6 rows x 38 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-4 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1f2bf379-70dd-41be-86c3-a63f2deb7c7f',\n",
       " 'f659ea05-a658-497d-9b2a-eb89e74d5403',\n",
       " '5e17a0f5-0a18-4a83-821a-31dd3fd014cd',\n",
       " 'c9b6de20-4b5c-4a87-88ce-482b2049a45c',\n",
       " 'c29e7a06-72de-4b77-bc5d-5568145f10eb',\n",
       " '0192c19c-eb0a-447b-82f1-740f32eb004a']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1f2bf379-70dd-41be-86c3-a63f2deb7c7f</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.184</td>\n",
       "      <td>19.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f659ea05-a658-497d-9b2a-eb89e74d5403</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.542</td>\n",
       "      <td>22.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5e17a0f5-0a18-4a83-821a-31dd3fd014cd</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c9b6de20-4b5c-4a87-88ce-482b2049a45c</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c29e7a06-72de-4b77-bc5d-5568145f10eb</td>\n",
       "      <td>ReadingGlasses</td>\n",
       "      <td>0.019</td>\n",
       "      <td>18.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0192c19c-eb0a-447b-82f1-740f32eb004a</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  1f2bf379-70dd-41be-86c3-a63f2deb7c7f              NoGlasses   \n",
       "1  f659ea05-a658-497d-9b2a-eb89e74d5403              NoGlasses   \n",
       "2  5e17a0f5-0a18-4a83-821a-31dd3fd014cd         ReadingGlasses   \n",
       "3  c9b6de20-4b5c-4a87-88ce-482b2049a45c              NoGlasses   \n",
       "4  c29e7a06-72de-4b77-bc5d-5568145f10eb         ReadingGlasses   \n",
       "5  0192c19c-eb0a-447b-82f1-740f32eb004a              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                           0.184                19.0                  male  \n",
       "1                           0.542                22.0                female  \n",
       "2                           0.000                25.0                  male  \n",
       "3                           0.000                23.0                female  \n",
       "4                           0.019                18.0                female  \n",
       "5                           0.000                19.0                female  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure 认知服务-人脸演示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试：人脸验证(难)\n",
    "\n",
    "人脸相似度？有没有试下？\n",
    "\n",
    ">* API人脸文档中最重要的组成部分：\n",
    " >>* Request URL？\n",
    " >>* Http Method？\n",
    " >>* 参数？\n",
    "\n",
    "----\n",
    ">* 不同的人脸对比数据\n",
    "  >>* 人脸验证关键数据？\n",
    "  >>* 根据哪些数据证明是同一个人？\n",
    "    \n",
    "----\n",
    ">* 具体步骤：\n",
    "  >>* 1、Create 请求成功200 返回空字符串\n",
    "  >>* 2、Add face 请求成功200 返回persistedFaceId\n",
    "  >>* 3、Detect 准备 被检测人 人脸的id\n",
    "  >>* 4、Find similars 返回相似置信度\n",
    "  >>* 附加：get查看facelists\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'zifei03' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-9485f9688384>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mfaceListId\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"zifei03\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# 1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0murl_01\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mzifei03\u001b[0m \u001b[1;31m# string 拼接\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;31m# 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0murl_02\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/%s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzifei03\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'zifei03' is not defined"
     ]
    }
   ],
   "source": [
    "# 字符串拼接练习\n",
    "faceListId = \"zifei03\"\n",
    "# 1\n",
    "url_01 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/\" + zifei03 # string 拼接\n",
    "# 2\n",
    "url_02 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/%s\" %(zifei03)\n",
    "# 3 \n",
    "url_03 = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/{}\".format(zifei03)\n",
    "print(url_01,'\\n',url_02,'\\n',url_03)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  list列表\n",
    "# faceListId\n",
    "faceListId ='zifei03'  # 学生填写设置人脸列表ID\n",
    "create_facelists_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/{}\"# 学生填写 ☆ 注意此条url修改\n",
    "subscription_key = \"9415313ee78e4d79a0d6cf603d0177f0\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "data = {\n",
    "    \"name\": \"外国女人\",\n",
    "    \"userData\": \"一个穿白衣的外国女人\",\n",
    "    \"recognitionModel\": \"recognition_03\",\n",
    "    \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url =\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03\" # 学生填写\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '178f214c-404d-497b-b12f-d5a8179a4bcb',\n",
       "   'userData': '丘小峰'},\n",
       "  {'persistedFaceId': '9ad0959a-67f6-4b37-b97b-47b681a62828',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'ed46c60b-3e8b-48a0-b684-adfd0e927648',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '06604967-1ab2-4a29-a6cb-f56ef2ad978a',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '3bb6f584-a741-4980-9ebd-55718fc985b8',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'fbe1be1c-0bb1-466b-8638-129577fd2f78',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '36edec47-85e9-4d02-ad1a-91c68d293eeb',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '234dbff0-0c9c-43c3-b3b6-0ef58181010d',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '6522b2e9-6522-4009-a348-87266ae38bd9',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '675c72e7-2d13-4ace-b1ee-187983b7551a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '9fbfc6db-64ff-448e-bff5-d57a4e61aec2',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'dc973a9b-9034-4d09-9d0b-d06756f0fdbe',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '4c47fe18-3b82-45f8-9a2d-f4fc17f8b775',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '34648626-4856-4a66-b998-6a1fe8f430db',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '1e38ec36-a0f7-487f-954d-c9479cbf6ce7',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '7ef61c15-d55f-44f9-9b53-2c0ca6adac87',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4299ce08-67d2-4c5e-9fd9-86a062be7893',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '8d28752b-990c-4a38-8816-eb10544d466d',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': 'a91918bd-a97f-498b-b58e-517d07a372d8',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '616cfcae-abb5-484b-8b9a-bdbcfc34ee47',\n",
       "   'userData': '卢继志'}],\n",
       " 'faceListId': 'zifei03',\n",
       " 'name': '外国女人',\n",
       " 'userData': '一个穿白衣的外国女人'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add face 请求成功200 返回persistedFaceId\n",
    "> 我们通过上面的步骤建好了一个脸的列表，接下来我们要给这个列表添加脸了！把我们想要对比的脸存进列表吧\n",
    "- [添加人脸进列表api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/facelist/addfacefromurl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "add_face_url =\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03/persistedfaces\"\n",
    "\n",
    "\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = \"https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1\"\n",
    "\n",
    "params_add_face={\n",
    "    \"faceListID\":\"zifei03\",\n",
    "    \"userData\":\"丘小峰\"\n",
    "}\n",
    "\n",
    "r_add_face = requests.post(add_face_url,headers=headers,params=params_add_face,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': '178f214c-404d-497b-b12f-d5a8179a4bcb',\n",
       "   'userData': '丘小峰'},\n",
       "  {'persistedFaceId': '9ad0959a-67f6-4b37-b97b-47b681a62828',\n",
       "   'userData': '丘天惠'},\n",
       "  {'persistedFaceId': 'ed46c60b-3e8b-48a0-b684-adfd0e927648',\n",
       "   'userData': '林嘉茵'},\n",
       "  {'persistedFaceId': '06604967-1ab2-4a29-a6cb-f56ef2ad978a',\n",
       "   'userData': '汤玲萍'},\n",
       "  {'persistedFaceId': '3bb6f584-a741-4980-9ebd-55718fc985b8',\n",
       "   'userData': '曾雯燕'},\n",
       "  {'persistedFaceId': 'fbe1be1c-0bb1-466b-8638-129577fd2f78',\n",
       "   'userData': '谢依希'},\n",
       "  {'persistedFaceId': '36edec47-85e9-4d02-ad1a-91c68d293eeb',\n",
       "   'userData': '杨悦聪'},\n",
       "  {'persistedFaceId': '234dbff0-0c9c-43c3-b3b6-0ef58181010d',\n",
       "   'userData': '周雨'},\n",
       "  {'persistedFaceId': '6522b2e9-6522-4009-a348-87266ae38bd9',\n",
       "   'userData': '刘瑜鹏'},\n",
       "  {'persistedFaceId': '675c72e7-2d13-4ace-b1ee-187983b7551a',\n",
       "   'userData': '陈嘉仪'},\n",
       "  {'persistedFaceId': '9fbfc6db-64ff-448e-bff5-d57a4e61aec2',\n",
       "   'userData': '徐旖芊'},\n",
       "  {'persistedFaceId': 'dc973a9b-9034-4d09-9d0b-d06756f0fdbe',\n",
       "   'userData': '刘心如'},\n",
       "  {'persistedFaceId': '4c47fe18-3b82-45f8-9a2d-f4fc17f8b775',\n",
       "   'userData': '刘宇'},\n",
       "  {'persistedFaceId': '34648626-4856-4a66-b998-6a1fe8f430db',\n",
       "   'userData': '李婷'},\n",
       "  {'persistedFaceId': '1e38ec36-a0f7-487f-954d-c9479cbf6ce7',\n",
       "   'userData': '黄智毅'},\n",
       "  {'persistedFaceId': '7ef61c15-d55f-44f9-9b53-2c0ca6adac87',\n",
       "   'userData': '黄慧文'},\n",
       "  {'persistedFaceId': '4299ce08-67d2-4c5e-9fd9-86a062be7893',\n",
       "   'userData': '张铭睿'},\n",
       "  {'persistedFaceId': '8d28752b-990c-4a38-8816-eb10544d466d',\n",
       "   'userData': '陈婷'},\n",
       "  {'persistedFaceId': 'a91918bd-a97f-498b-b58e-517d07a372d8',\n",
       "   'userData': '洪可凡'},\n",
       "  {'persistedFaceId': '616cfcae-abb5-484b-8b9a-bdbcfc34ee47',\n",
       "   'userData': '卢继志'},\n",
       "  {'persistedFaceId': '991e0665-89da-4441-99cf-c1fb0fef0a11',\n",
       "   'userData': '丘小峰'}],\n",
       " 'faceListId': 'zifei03',\n",
       " 'name': '外国女人',\n",
       " 'userData': '一个穿白衣的外国女人'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03\"\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaceId': '991e0665-89da-4441-99cf-c1fb0fef0a11'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 扩展内容，封装成函数方便多次使用 *\n",
    "> 我们要添加多张脸，但是为了减少代码量，我们可以把代码封装成函数，避免每次都要写一大堆代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = img_url\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code #返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Autumnhui.jpg\",\"丘天惠\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/L-Tony-info.jpg\",\"林嘉茵\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/TLINGP.jpg\",\"汤玲萍\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/WenYanZeng.jpg\",\"曾雯燕\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/XIEIC.jpg\",\"谢依希\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/YuecongYang.png\",\"杨悦聪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/Zoezhouyu.jpg\",\"周雨\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/crayon-heimi.jpg\",\"刘瑜鹏\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/jiayichen.jpg\",\"陈嘉仪\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/kg2000.jpg\",\"徐旖芊\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuxinrujiayou.jpg\",\"刘心如\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/liuyu19.png\",\"刘宇\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/ltco.jpg\",\"李婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lucaszy.jpg\",\"黄智毅\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/pingzi0211.jpg\",\"黄慧文\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/shmimy-cn.jpg\",\"张铭睿\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/yichenting.jpg\",\"陈婷\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/coco022.jpg\",\"洪可凡\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/lujizhi.png\",\"卢继志\")\n",
    "AddFace(\"http://huangjieqi.gitee.io/picture_storage/zzlhyy.jpg\",\"张梓乐\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'headers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-48c17e41cd9d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 检查你的facelist的信息\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mget_facelist_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mr_get_facelist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_facelist_url\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mr_get_facelist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'headers' is not defined"
     ]
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03\"\n",
    "r_get_facelist = requests.get(get_facelist_url,headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '178f214c-404d-497b-b12f-d5a8179a4bcb',\n",
       "  'userData': '丘小峰'},\n",
       " {'persistedFaceId': '9ad0959a-67f6-4b37-b97b-47b681a62828',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'ed46c60b-3e8b-48a0-b684-adfd0e927648',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '06604967-1ab2-4a29-a6cb-f56ef2ad978a',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '3bb6f584-a741-4980-9ebd-55718fc985b8',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'fbe1be1c-0bb1-466b-8638-129577fd2f78',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '36edec47-85e9-4d02-ad1a-91c68d293eeb',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '234dbff0-0c9c-43c3-b3b6-0ef58181010d', 'userData': '周雨'},\n",
       " {'persistedFaceId': '6522b2e9-6522-4009-a348-87266ae38bd9',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '675c72e7-2d13-4ace-b1ee-187983b7551a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '9fbfc6db-64ff-448e-bff5-d57a4e61aec2',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': 'dc973a9b-9034-4d09-9d0b-d06756f0fdbe',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '4c47fe18-3b82-45f8-9a2d-f4fc17f8b775', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '34648626-4856-4a66-b998-6a1fe8f430db', 'userData': '李婷'},\n",
       " {'persistedFaceId': '1e38ec36-a0f7-487f-954d-c9479cbf6ce7',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '7ef61c15-d55f-44f9-9b53-2c0ca6adac87',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '4299ce08-67d2-4c5e-9fd9-86a062be7893',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '8d28752b-990c-4a38-8816-eb10544d466d', 'userData': '陈婷'},\n",
       " {'persistedFaceId': 'a91918bd-a97f-498b-b58e-517d07a372d8',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '616cfcae-abb5-484b-8b9a-bdbcfc34ee47',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '991e0665-89da-4441-99cf-c1fb0fef0a11',\n",
       "  'userData': '丘小峰'}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ed46c60b-3e8b-48a0-b684-adfd0e927648'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 键/值\n",
    "for i in faceId:\n",
    "#     print(i)\n",
    " if i[\"userData\"] == \"林嘉茵\":\n",
    "        faceId_02 = i['persistedFaceId']\n",
    "faceId_02\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': '178f214c-404d-497b-b12f-d5a8179a4bcb',\n",
       "  'userData': '丘小峰'},\n",
       " {'persistedFaceId': '9ad0959a-67f6-4b37-b97b-47b681a62828',\n",
       "  'userData': '丘天惠'},\n",
       " {'persistedFaceId': 'ed46c60b-3e8b-48a0-b684-adfd0e927648',\n",
       "  'userData': '林嘉茵'},\n",
       " {'persistedFaceId': '06604967-1ab2-4a29-a6cb-f56ef2ad978a',\n",
       "  'userData': '汤玲萍'},\n",
       " {'persistedFaceId': '3bb6f584-a741-4980-9ebd-55718fc985b8',\n",
       "  'userData': '曾雯燕'},\n",
       " {'persistedFaceId': 'fbe1be1c-0bb1-466b-8638-129577fd2f78',\n",
       "  'userData': '谢依希'},\n",
       " {'persistedFaceId': '36edec47-85e9-4d02-ad1a-91c68d293eeb',\n",
       "  'userData': '杨悦聪'},\n",
       " {'persistedFaceId': '234dbff0-0c9c-43c3-b3b6-0ef58181010d', 'userData': '周雨'},\n",
       " {'persistedFaceId': '6522b2e9-6522-4009-a348-87266ae38bd9',\n",
       "  'userData': '刘瑜鹏'},\n",
       " {'persistedFaceId': '675c72e7-2d13-4ace-b1ee-187983b7551a',\n",
       "  'userData': '陈嘉仪'},\n",
       " {'persistedFaceId': '9fbfc6db-64ff-448e-bff5-d57a4e61aec2',\n",
       "  'userData': '徐旖芊'},\n",
       " {'persistedFaceId': 'dc973a9b-9034-4d09-9d0b-d06756f0fdbe',\n",
       "  'userData': '刘心如'},\n",
       " {'persistedFaceId': '4c47fe18-3b82-45f8-9a2d-f4fc17f8b775', 'userData': '刘宇'},\n",
       " {'persistedFaceId': '34648626-4856-4a66-b998-6a1fe8f430db', 'userData': '李婷'},\n",
       " {'persistedFaceId': '1e38ec36-a0f7-487f-954d-c9479cbf6ce7',\n",
       "  'userData': '黄智毅'},\n",
       " {'persistedFaceId': '7ef61c15-d55f-44f9-9b53-2c0ca6adac87',\n",
       "  'userData': '黄慧文'},\n",
       " {'persistedFaceId': '4299ce08-67d2-4c5e-9fd9-86a062be7893',\n",
       "  'userData': '张铭睿'},\n",
       " {'persistedFaceId': '8d28752b-990c-4a38-8816-eb10544d466d', 'userData': '陈婷'},\n",
       " {'persistedFaceId': 'a91918bd-a97f-498b-b58e-517d07a372d8',\n",
       "  'userData': '洪可凡'},\n",
       " {'persistedFaceId': '616cfcae-abb5-484b-8b9a-bdbcfc34ee47',\n",
       "  'userData': '卢继志'},\n",
       " {'persistedFaceId': '991e0665-89da-4441-99cf-c1fb0fef0a11',\n",
       "  'userData': '丘小峰'}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceId =  r_get_facelist.json()['persistedFaces']\n",
    "faceId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1e38ec36-a0f7-487f-954d-c9479cbf6ce7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1e38ec36-a0f7-487f-954d-c9479cbf6ce7'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in faceId:\n",
    "#     print(item)\n",
    "    if item['userData'] == '黄智毅':\n",
    "        print(item['persistedFaceId'])\n",
    "        delate_face = item['persistedFaceId']\n",
    "delate_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delate face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_key = '9415313ee78e4d79a0d6cf603d0177f0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'subscription_key' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-0d86fb8a1ea7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdelete_face_url\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mdelete_face_url\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;34m'https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03/persistedfaces'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0msubscription_key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'subscription_key' is not defined"
     ]
    }
   ],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = '1e38ec36-a0f7-487f-954d-c9479cbf6ce7'\n",
    "delete_face_url =delete_face_url ='https://api-gzf123.cognitiveservices.azure.com/face/v1.0/facelists/zifei03/persistedfaces'\n",
    "\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\n",
    "\n",
    "\n",
    "persistedFaceId = delete_face\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url.format(faceListId,persistedFaceId),headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'r_delete_face' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-e9bfd2d26d27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mr_delete_face\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'r_delete_face' is not defined"
     ]
    }
   ],
   "source": [
    "r_delete_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': {'code': '401',\n",
       "  'message': 'Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n",
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find similars 返回相似置信度\n",
    "- [监测人脸相似度api文档](https://docs.microsoft.com/zh-cn/rest/api/cognitiveservices/face/face/findsimilar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_key = '9415313ee78e4d79a0d6cf603d0177f0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': {'code': '401',\n",
       "  'message': 'Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'}}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "findsimilars_url = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "# 请求正文 faceID需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"1e38ec36-a0f7-487f-954d-c9479cbf6ce7\", #取上方的faceID\n",
    "    \"faceListId\": \"zifei03\",\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchFace\" #matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)\n",
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "外国女人 = response.json()[0][\"faceId\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': {'code': '401',\n",
       "  'message': 'Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'}}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findsimilars_url = \"https://api-hjq.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "# 请求正文 faceID需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"036c5db1-83d5-4d21-8917-d33294080b39\", #取上方的faceID\n",
    "    \"faceListId\": \"list_003\",\n",
    "    \"maxNumOfCandidatesReturned\": 10,\n",
    "    \"mode\": \"matchFace\" #matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)\n",
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [401]>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error': {'code': '401',\n",
       "  'message': 'Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'}}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'persistedFaces'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-8f37fcdf4ddd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#facelist里面的数据\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mfaceListId_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson_normalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr_get_facelist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"persistedFaces\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m# 升级pandas才能运行\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mfaceListId_df\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'persistedFaces'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#facelist里面的数据\n",
    "faceListId_df = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "faceListId_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 返回相似度的数据\n",
    "find_df = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "find_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.merge(faceListId_df, find_df,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设计人脸识别门禁/打卡/签到 小程序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face++ FaceSets 实践\n",
    "\n",
    "\n",
    ">* 1. FaceSet Create\n",
    ">* 2. FaceSet GetDetail\n",
    ">* 3. FaceSet AddFace\n",
    ">* 4. FaceSet RemoveFace\n",
    ">* 5. FaceSet Update\n",
    ">* 6. Compare Face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_secret = '9m5r8GyQF5bscYWr28UFL6vOw--8c6T9'\n",
    "api_key = '4DL_jFINmFehwx3N_ojRps8sri-9g0v5'  # Replace with a valid Subscription Key here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"网新一班人脸集合\"\n",
    "outer_id = \"00001\"\n",
    "user_data = \"52人，20男生，32女生\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '9c6f8920484a86fc2a1707a3b9396f53',\n",
       " 'time_used': 179,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603422397,6dda6cd0-738a-4367-9fee-e89930e54801',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': []}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '9c6f8920484a86fc2a1707a3b9396f53',\n",
       " 'tags': '',\n",
       " 'time_used': 149,\n",
       " 'user_data': '52人，20男生，32女生',\n",
       " 'display_name': '网新一班人脸集合',\n",
       " 'face_tokens': [],\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603422454,a7d21082-d649-4085-8de8-856d907d5b5d',\n",
       " 'outer_id': '00001'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'9c6f8920484a86fc2a1707a3b9396f53',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(AddFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '9c6f8920484a86fc2a1707a3b9396f53',\n",
       " 'time_used': 78,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603422748,8f4a8542-52d8-44d8-9631-09414b9c753d',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': [{'reason': 'INVALID_FACE_TOKEN',\n",
       "   'face_token': 'b0407b9e803ebd39d511cd7956fd5bf5'}]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'9c6f8920484a86fc2a1707a3b9396f53',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '9c6f8920484a86fc2a1707a3b9396f53',\n",
       " 'face_removed': 0,\n",
       " 'time_used': 135,\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603422809,d16d5d24-5170-4a09-9b69-089e56819e30',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': [{'reason': 'FACE_NOT_IN_FACESET',\n",
       "   'face_token': 'b0407b9e803ebd39d511cd7956fd5bf5'}]}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'9c6f8920484a86fc2a1707a3b9396f53',\n",
    "    'user_data':\"53人，21男生，32女生\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '9c6f8920484a86fc2a1707a3b9396f53',\n",
       " 'request_id': '1603422825,cb437e1e-b3d8-4bee-887a-05cfd6046b56',\n",
       " 'time_used': 66,\n",
       " 'outer_id': '00001'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "liudehua01 = \"https://gss0.baidu.com/9fo3dSag_xI4khGko9WTAnF6hhy/zhidao/pic/item/7c1ed21b0ef41bd57f7f20ff57da81cb39db3d89.jpg\"\n",
    "liudehua02 = \"https://tse3-mm.cn.bing.net/th/id/OIP.Xz3HbYZeNrdUnGJ7vXNzsQHaKO?pid=Api&rs=1\"\n",
    "wangzulan = \"https://tse3-mm.cn.bing.net/th/id/OIP.ZnXeGoVYT4jQudiPOGZn3QAAAA?pid=Api&rs=1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url1':liudehua01,\n",
    "    'image_url2':wangzulan\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faces1': [{'face_rectangle': {'width': 824,\n",
       "    'top': 871,\n",
       "    'left': 1114,\n",
       "    'height': 824},\n",
       "   'face_token': 'c51f21f739f2eba0415f44bf8d93b680'}],\n",
       " 'faces2': [{'face_rectangle': {'width': 86,\n",
       "    'top': 91,\n",
       "    'left': 65,\n",
       "    'height': 86},\n",
       "   'face_token': '0a22e3b5e5d03ecd20daa749f1314a10'}],\n",
       " 'time_used': 5062,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-5': 73.975, '1e-4': 69.101},\n",
       " 'confidence': 26.085,\n",
       " 'image_id2': 'g6kg8zfyOouG6ftP+GvEfg==',\n",
       " 'image_id1': 'KIOXEC2V/MyL4zuopAcNig==',\n",
       " 'request_id': '1603422619,4439d0d8-ae7b-4448-8747-7c451773e243'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = liudehua01\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1603422639,1694392a-025a-4c88-be14-3d7738434b0b',\n",
       " 'time_used': 1215,\n",
       " 'faces': [{'face_token': 'fa96b76d6914291e37c5b4358387b8d3',\n",
       "   'face_rectangle': {'top': 871, 'left': 1114, 'width': 824, 'height': 824},\n",
       "   'attributes': {'gender': {'value': 'Male'},\n",
       "    'age': {'value': 59},\n",
       "    'smile': {'value': 99.998, 'threshold': 50.0},\n",
       "    'emotion': {'anger': 0.0,\n",
       "     'disgust': 0.047,\n",
       "     'fear': 0.0,\n",
       "     'happiness': 99.945,\n",
       "     'neutral': 0.0,\n",
       "     'sadness': 0.007,\n",
       "     'surprise': 0.0}}}],\n",
       " 'image_id': 'KIOXEC2V/MyL4zuopAcNig==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加入人脸集合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 百度智能云实践  \n",
    ">* 1. 人脸检测\n",
    ">* 2. 人脸检测与属性分析\n",
    ">* 3. 添获取face_token\n",
    ">* 4. 人脸对比\n",
    ">* 5. 创建用户组\n",
    ">* 6. 任人脸注册\n",
    ">* 7. 获取人脸列表\n",
    ">* 8. 删除用户组\n",
    " \n",
    "参考：[百度智能云人脸识别文档](https://cloud.baidu.com/doc/FACE/s/yk37c1u4t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "acAPI key：'QDyO6xZw9YQS7rZgcgflHabY'\n",
    "secret key：'4CaaqEHNI0MzrRA0R8Mr5x0s9lItZg41'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 人脸检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'refresh_token': '25.c96f6e1434a57a056744e332eb03ff64.315360000.1918868706.282335-22868390', 'expires_in': 2592000, 'session_key': '9mzdDcXrEO8RXcH8feUdU0jn0AoqSCBENYLUjOPK3awP2NYFGogdUOYPrPcFqsQNPI4B+zhNVHlpMhcdnXQhkXcGmfBRow==', 'access_token': '24.638976d3f52fbfa4dfbbb26c5ae9c271.2592000.1606100706.282335-22868390', 'scope': 'public brain_all_scope vis-classify_animal brain_realtime_logo brain_animal_classify vis-faceverify_faceverify_h5-face-liveness brain_advanced_general_classify brain_custom_dish vis-faceverify_FACE_V3 unit_理解与交互V2 vis-faceverify_idl_face_merge solution_face vis-faceverify_FACE_EFFECT vis-faceverify_face_feature_sdk wise_adapt lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian ApsMisTest_Test权限 vis-classify_flower lpq_开放 cop_helloScope ApsMis_fangdi_permission smartapp_snsapi_base smartapp_mapp_dev_manage iop_autocar oauth_tp_app smartapp_smart_game_openapi oauth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi smartapp_opensource_recapi fake_face_detect_开放Scope vis-ocr_虚拟人物助理 idl-video_虚拟人物助理 smartapp_component', 'session_secret': '913317808f4af6d7ce2a9c2b54196064'}\n"
     ]
    }
   ],
   "source": [
    " # encoding:utf-8\n",
    "import requests \n",
    "\n",
    "# client_id 为官网获取的AK， client_secret 为官网获取的SK\n",
    "host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={}&client_secret={}'\n",
    "client_id = \"QDyO6xZw9YQS7rZgcgflHabY\"\n",
    "client_secret = \"4CaaqEHNI0MzrRA0R8Mr5x0s9lItZg41\"\n",
    "response = requests.get(host.format(client_id, client_secret))\n",
    "if response:\n",
    "    print(response.json())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 人脸检测与属性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 2589943525454,\n",
       " 'timestamp': 1603523314,\n",
       " 'cached': 0,\n",
       " 'result': {'face_num': 1,\n",
       "  'face_list': [{'face_token': '79c265847c2a774d717aabfee9d7c09e',\n",
       "    'location': {'left': 133.16,\n",
       "     'top': 195.98,\n",
       "     'width': 194,\n",
       "     'height': 178,\n",
       "     'rotation': 1},\n",
       "    'face_probability': 1,\n",
       "    'angle': {'yaw': 12.03, 'pitch': 17.61, 'roll': -4.65},\n",
       "    'face_shape': {'type': 'oval', 'probability': 0.49},\n",
       "    'face_type': {'type': 'human', 'probability': 0.86}}]}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.人脸检测与属性分析 \n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/detect\"\n",
    "\n",
    "params = \"{\\\"image\\\":\\\"https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1\\\",\\\"image_type\\\":\\\"URL\\\",\\\"face_field\\\":\\\"faceshape,facetype\\\"}\"\n",
    "# image_type 图片类型：BASE64；URL；FACE_TOKEN。\n",
    "\n",
    "access_token = '24.6d684a22feb8384590448aa0f4edcb8e.2592000.1605596154.282335-22838595' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取face_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'refresh_token': '25.4531640f19fa4d7ca59be93b01a0c409.315360000.1918884055.282335-22868390', 'expires_in': 2592000, 'session_key': '9mzdX+PXkLNJJnaCsjvW0VDsQHdD6On+st02gPQpEwBE9Vn0qe/xXIxO3ULz14b8sNKyFshsT8LF3jgWZLmQSQuKzKuqFA==', 'access_token': '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390', 'scope': 'public brain_all_scope vis-classify_animal brain_realtime_logo brain_animal_classify vis-faceverify_faceverify_h5-face-liveness brain_advanced_general_classify brain_custom_dish vis-faceverify_FACE_V3 unit_理解与交互V2 vis-faceverify_idl_face_merge solution_face vis-faceverify_FACE_EFFECT vis-faceverify_face_feature_sdk wise_adapt lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian ApsMisTest_Test权限 vis-classify_flower lpq_开放 cop_helloScope ApsMis_fangdi_permission smartapp_snsapi_base smartapp_mapp_dev_manage iop_autocar oauth_tp_app smartapp_smart_game_openapi oauth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi smartapp_opensource_recapi fake_face_detect_开放Scope vis-ocr_虚拟人物助理 idl-video_虚拟人物助理 smartapp_component', 'session_secret': 'b11abe5db9840b1b1b37034549b6ed26'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'refresh_token': '25.4531640f19fa4d7ca59be93b01a0c409.315360000.1918884055.282335-22868390',\n",
       " 'expires_in': 2592000,\n",
       " 'session_key': '9mzdX+PXkLNJJnaCsjvW0VDsQHdD6On+st02gPQpEwBE9Vn0qe/xXIxO3ULz14b8sNKyFshsT8LF3jgWZLmQSQuKzKuqFA==',\n",
       " 'access_token': '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390',\n",
       " 'scope': 'public brain_all_scope vis-classify_animal brain_realtime_logo brain_animal_classify vis-faceverify_faceverify_h5-face-liveness brain_advanced_general_classify brain_custom_dish vis-faceverify_FACE_V3 unit_理解与交互V2 vis-faceverify_idl_face_merge solution_face vis-faceverify_FACE_EFFECT vis-faceverify_face_feature_sdk wise_adapt lebo_resource_base lightservice_public hetu_basic lightcms_map_poi kaidian_kaidian ApsMisTest_Test权限 vis-classify_flower lpq_开放 cop_helloScope ApsMis_fangdi_permission smartapp_snsapi_base smartapp_mapp_dev_manage iop_autocar oauth_tp_app smartapp_smart_game_openapi oauth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi smartapp_opensource_recapi fake_face_detect_开放Scope vis-ocr_虚拟人物助理 idl-video_虚拟人物助理 smartapp_component',\n",
       " 'session_secret': 'b11abe5db9840b1b1b37034549b6ed26'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取refresh_token\n",
    "# encoding:utf-8\n",
    "import requests \n",
    "\n",
    "# client_id 为官网获取的AK， client_secret 为官网获取的SK\n",
    "host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=QDyO6xZw9YQS7rZgcgflHabY&client_secret=4CaaqEHNI0MzrRA0R8Mr5x0s9lItZg41'\n",
    "response = requests.get(host)\n",
    "if response:\n",
    "    print(response.json())\n",
    "response.json()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 人脸对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 9955555515051,\n",
       " 'timestamp': 1603524150,\n",
       " 'cached': 0,\n",
       " 'result': {'score': 21.48541641,\n",
       "  'face_list': [{'face_token': '79c265847c2a774d717aabfee9d7c09e'},\n",
       "   {'face_token': '93d2501e1cb1685c26107d0b19cc854a'}]}}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.人脸对比\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/match\"\n",
    "\n",
    "params = \"[{\\\"image\\\": \\\"https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1\\\", \\\"image_type\\\": \\\"URL\\\", \\\"face_type\\\": \\\"CERT\\\", \\\"quality_control\\\": \\\"LOW\\\"}, {\\\"image\\\": \\\"https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1603015508446&di=eee4e2c852d804bc3b80a719df3df9ef&imgtype=0&src=http%3A%2F%2Fimg2-cloud.itouchtv.cn%2Ftvtouchtv%2Fimage%2F20170914%2F1505363583630378.jpg\\\", \\\"image_type\\\": \\\"URL\\\", \\\"face_type\\\": \\\"LIVE\\\", \\\"quality_control\\\": \\\"LOW\\\"}]\"\n",
    "# face_type 人脸的类型 LIVE；IDCARD；WATERMARK；CERT；INFRARED。\n",
    "\n",
    "access_token = '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建用户组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 19984359494,\n",
       " 'timestamp': 1603524160,\n",
       " 'cached': 0,\n",
       " 'result': None}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.创建用户组\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/faceset/group/add\"\n",
    "\n",
    "params = \"{\\\"group_id\\\":\\\"group2\\\"}\"\n",
    "access_token = '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 人脸注册"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 7965654500125,\n",
       " 'timestamp': 1603524450,\n",
       " 'cached': 0,\n",
       " 'result': {'face_token': '91cf0a5aa45b0371989e56760b30548c',\n",
       "  'location': {'left': 186.93,\n",
       "   'top': 99.21,\n",
       "   'width': 121,\n",
       "   'height': 118,\n",
       "   'rotation': 1}}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.人脸注册\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/faceset/user/add\"\n",
    "\n",
    "params = \"{\\\"image\\\":\\\"91cf0a5aa45b0371989e56760b30548c\\\",\\\"image_type\\\":\\\"FACE_TOKEN\\\",\\\"group_id\\\":\\\"group1\\\",\\\"user_id\\\":\\\"user1\\\",\\\"user_info\\\":\\\"abc\\\",\\\"quality_control\\\":\\\"LOW\\\",\\\"liveness_control\\\":\\\"NORMAL\\\"}\"\n",
    "access_token = '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取人脸列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 1565941510189,\n",
       " 'timestamp': 1603524659,\n",
       " 'cached': 0,\n",
       " 'result': {'face_list': [{'face_token': '91cf0a5aa45b0371989e56760b30548c',\n",
       "    'ctime': '2020-10-23 17:46:47'}]}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5.获取用户人脸列表\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/faceset/face/getlist\"\n",
    "\n",
    "params = \"{\\\"user_id\\\":\\\"user1\\\",\\\"group_id\\\":\\\"group2\\\"}\"\n",
    "access_token = '24.6d684a22feb8384590448aa0f4edcb8e.2592000.1605596154.282335-22838595' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除用户组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'error_code': 0,\n",
       " 'error_msg': 'SUCCESS',\n",
       " 'log_id': 3535650013579,\n",
       " 'timestamp': 1603524723,\n",
       " 'cached': 0,\n",
       " 'result': None}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6.删除用户组\n",
    "request_url = \"https://aip.baidubce.com/rest/2.0/face/v3/faceset/group/delete\"\n",
    "\n",
    "params = \"{\\\"group_id\\\":\\\"group1\\\"}\"\n",
    "access_token = '24.d9c55965e1770765fc5707183a796d9b.2592000.1606116055.282335-22868390' # 调用鉴权接口获取的token\n",
    "request_url = request_url + \"?access_token=\" + access_token\n",
    "headers = {'content-type': 'application/json'}\n",
    "response = requests.post(request_url, data=params, headers=headers)\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸识别社会/政治使用思考\n",
    "> 人脸识别的 API比较\n",
    ">> * [Face Off: Confronting Bias in Face Recognition AI](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    ">> * [为什么世界现在需要道德的面部识别](https://www.kairos.com/blog/why-the-world-needs-ethical-facial-recognition-now)\n",
    "    * 到2023年，全球人脸识别市场的价值估计接近100亿美元，复合年增长率为16.8％。市场背后的主要增长动力是监视市场的发展以及生物识别技术领域的政府支出。但是，面部识别的使用在其他领域也有很大贡献，例如帮助企业打击消费者欺诈，满足动态法规遵从性以及提供可获利的客户体验。\n",
    "\n",
    "-----\n",
    "> 人脸识别的偏差及API 政治经济学 \n",
    ">> * [对抗：面对面部识别AI中的偏见](https://www.kairos.com/blog/face-off-confronting-bias-in-face-recognition-ai)\n",
    "    * 发生了什么？\n",
    "        * “编码注视”或算法偏差的研究: 浅肤色男性的错误率为0.8％ ?  深色皮肤女性的错误率高达34.7％  ? (Microsoft，IBM和Face ++)\n",
    "    * 用户的反应范围从惊奇和赞美到不悦和冒犯\n",
    "        * 我们承认目前提供的种族分类（黑人，白人，亚裔，西班牙裔，其他则不足以代表丰富多样，发展迅速的文化和种族挂毯\n",
    "    * 该怎么办？\n",
    "        * 改善数据\n",
    "        * 寻求持续的反馈    \n",
    "        \n",
    ">>  * [科技行业没有应对面部识别偏见的计划](https://www.theverge.com/2018/7/26/17616290/facial-recognition-ai-bias-benchmark-test) \n",
    ">>> 公司在做什么？(一些高科技大公司支持基准和法规)     \n",
    ">>> 我们如何解决偏见问题？    \n",
    ">>> 偏差不是唯一的问题\n",
    "\n",
    ">> * [面部识别尚未准备好给执法部门使用](https://techcrunch.com/2018/06/25/facial-recognition-software-is-not-ready-for-use-by-law-enforcement/)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉\n",
    "\n",
    "* 学习并完成所有Azure computer version 的API调用\n",
    "> * 分析远程图像    \n",
    "> * 分析本地图片    \n",
    "> * 生成缩略图    \n",
    "> * 提取印刷体文本和手写文本    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"people_\", \"score\": 0.85546875, \"detail\": {\"celebrities\": []}}], \"color\": {\"dominantColorForeground\": \"White\", \"dominantColorBackground\": \"White\", \"dominantColors\": [\"White\", \"Black\"], \"accentColor\": \"696266\", \"isBwImg\": false, \"isBWImg\": false}, \"description\": {\"tags\": [\"person\", \"clothing\", \"woman\", \"posing\", \"young\", \"holding\", \"hair\", \"dress\", \"smiling\", \"sitting\", \"front\", \"standing\", \"wearing\", \"girl\", \"female\", \"shirt\", \"board\", \"surfing\"], \"captions\": [{\"text\": \"a woman posing for the camera\", \"confidence\": 0.973804316203875}]}, \"requestId\": \"8eb85d5b-c045-41be-b011-67a93324ec1a\", \"metadata\": {\"height\": 626, \"width\": 474, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://computervision-gzf.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"15f98ea70f74425382a7f6b768cd9915\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'others_', 'score': 0.00390625}, {'name': 'people_', 'score': 0.6484375}, {'name': 'people_show', 'score': 0.30078125}], 'color': {'dominantColorForeground': 'Black', 'dominantColorBackground': 'Black', 'dominantColors': ['Black', 'Grey'], 'accentColor': '485E74', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['person', 'outdoor', 'road', 'car', 'man', 'street', 'riding', 'holding', 'walking', 'talking', 'sitting', 'standing', 'wearing', 'young', 'parking', 'using', 'phone', 'suit', 'boy', 'girl', 'city', 'woman', 'meter'], 'captions': [{'text': 'a man riding on the back of a car', 'confidence': 0.5144281918165352}]}, 'requestId': '17373c2b-a3a4-444d-a63c-24a0cd263978', 'metadata': {'height': 5152, 'width': 2898, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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PIyIG6yxJU1b9HZ/hR+8rCS3ThfJRyWpEU4jB/OeM81SSwnerilcZ4ChW4H8kYhpBLRhDioFMcI2E0WOsoEnRkBPDOPQczhf0Q2CQgRsPrnH/4S4aI8bAwfyQZTfSVJkNjxrwPpCiMgw9w+BJIUtZk2o+6WIEY7GPgNeIIGoQK8SYMAptO8HZCkVJGjHGUrsqn+JGEMlEHcYgaAaotfl0NELtqkKsZYY+z8EzaKwYNtZmGGO5cW+Hr3z168yHAd93xDGQfJ7lR3LlEEVyguyW9E2LHwYW7YIheN6oLJ944Rn6oc+nvwoiFnEWZ3LpL5ry5N7mfQIkQ1vEHGv7hSwpFjEcFfn59JfyOZJL+qzoKSX/I+0+R++vgP9orMBf4v69+ywWh4ixhDAwny8YxpFx9AQfSQTSmOjHnmU/Fq29ElKira6BwOHB/JhUGvoRU9Wsra1RVw2QiJrL1qQmE1OTmqaqaJoGI0I/DhwVqbZyYKCxNSo2L95YMCizyQxs1sOHEIrgJ7PymhSxJp+qxhRdvmLEogiuMvkk1ZTVcqVeNtYiJTHMZhNu3brL69dv0fcdvlvixyEvBoWi9gOOzlGVnHB8v2QeAk2agiaGMHLh5DaXL57LhQlZgVjXOTFVRgjDkjEmbOECpLQbAEJJAI9uBh1hHTkGtFCO/FIhlJ/qkf8eVQLvCJBXsQL/cXzlq1/G+xFU6H2H7wNYR9s0WUmWIsMw0o89TixJFVdZ2rolIZzeWmdtMqXvR0IMxFaZzNbYOrmdZ94hgkbGokkPIdLUFWdPnODk1iYR2D08ZO/gkBA1cwfWUjkLYjFiaGuLj5FpVRMFQlJiTBkIoqWcBmMdkArgIcZQlHW2tMOCya00ghA1twe2cjhX0Y+B1966jV8uWfYLwjDioyf5gCYlERHNZXYSQIQkEYPNn7dYIjFzCV/+xjd5+qknstpRcyFeOYcawZIgeVQCsSSxR2Gdf9Ij4i4d9/NHJ7k+8rnKny7oj9aRjlaLHqH/VsEK/McxjoG2akgoTV2jM6FpWqazNZq6ofMjwzDgSjnedz3WGpq6xljh1OYmGgOH8wW78wXBB9Y2tzlx8gR93yOaMALjMOJDIGiicRUbG2uc2NpCjcXWFVEsaC7TnbOIKj5GnLXMJjUoRIXOByorVLUUzbtBNMtXnWuOK1wRS0qhfL+aFBNBI5XN7UVIEaNZQ2+aBmcdf/y1r7PolnTLJWn0jCnkVdn4jjT2SH2nxycxkMCoEjH4oSdKxBjD66+/zosf+wFS8hyz9QLWCtPGZP1/HPHeU1VlsejRkr4oBd9Z9s1JJB2d6mW5J3+NHCeFd878o3kA35VYvt9jBf4S29snmU1bkibaSUtdNYgItqyE9t6z7HraukGsMPQd1joaU7HsF4wpUoulaVsm5USera+zvr6BM44xDhATNYK4mlYMxkDnPXf2d7FYxhCpqxqxFisGY3Mvi/dYo2gMxJRPW2csxuWZtpBVboKiErGmySIXElXdEqNHNeFcnUVCKeGQd8g1IKVA267hU2LvwQPGZRbo+LK9ZxTUWChaeUMu91UTpKKvB7TwEsEY8ImhP+SlV1/lJ3/40zg7/e7tfSNYYxiHwJh8XttNuQWi9P+qR7Td0VpwQuUdWP9JB4DjNuCRiuDorZBJx1XkWIG/xImT22ysraFJaWqHaxq6vgcc09kEGT0hJiazKXVl6KzBujzT78OIH3pCChhnadopMSnWmcyuTxsYIIweMNjGUjmHMflUXnQdmpSqmeDqupz8gqvaPLt2IyaOxOhJKYM4GajbNstcUyTERFXXWCNY47DWEQnUrkZTIKWEdQ3WWkIYSTHhrC2sekJSwNQ1d2/dYhxHDJaEEvxI8GMeLcaAiuTJAxY1prQbRx19bs81Jfw44EzD2HsW8wW1zWPO9GhZL9lHYTJx1NRZTVhYfkGOe31VRQWk8ClHsDaPFPW5xJecqJD8/1FKotBjclBWZf9xrMBfYm02Y21tlktsDK5ui8KtwdUtMnpUM7HlTIOxI8YI1rmsSSeSYt5Oc00GyNGiSl23kBSNinFQNy2Tdq0szwS6+R5JhHa6RkyJOEZMZWibaV6BjQ6JNaFfYoGmnZKKEq5yhqiCdcpktg4CtbUF5AFIOJMrmqpqqaqG6HuW/RJroDKGmJTlomOtbXnrzWs0zZS3336b/Ye7eN/laYAmzm6uZ2GONYw+cXc+z3v2xmGdxZjcAqWY9Q0MkVFGHj7YIQSPtTmhWGNyEkmJlMastzdSJhXyJ87qsqp8jPo/7e4jYo4Tgmj5+twLHI8Bj6k/WZX9R7ECf4mqnlA3DaFb4lOkAqzNc+eqcriqomnyAoyIZN25MdRNzdpsLc+xyfvk1lR0830QoW3zzrpVpamqnCymM5q6RRC6fkEY6yzLreusB7CBqq5pmhmGRGQKcaAzgcZUVFWLqVwGmBgqMaiB6aQBYwvxp7kPVsncARbnHFVliabCmjrP+TXx9S9/k735AfvzJfceHHLt+nV2du6DapYCi+Hc1hY/9sKztJXh0mOPcbjs+KXPf5GbD3YZQy7ZTUzUTUtTVcQU8tquV6gq3rxxk2fe8xSSFIsQRRG1x5WAMTaLhjRrFSAdl/1A/tifIPVSyQMppeOSPvOPUiYe4JzL4JcjBmF18h/FCvwl2ukE4xzOOkxlmE6nxdIqA33SthjN8+Oqqama3GNXBcQpJTBlo845vF9irWU6abBFS0+MiBGa2tHUhpRAphNqaxi6JUfWeFJZ2nZCXdcgKWv4jaPd2KapG5KQPf1UiZo18mMY0ThgqEhi0JiIvsdaQaMgFkwCiWCN0k5nNG2LqvJwPufzv/f73Hr7HlXTsr6xzbmzZxgSNA42BNarGjHKE49d4NT2BmHoubi9zu4w0IaADy3Be4IfwVnayTpjv0Q0EWPk//ztf8a//+T7GXwkhsSQwIeI15RFfRq5tXsTtYqq5MUlFB9yRTUGT9QsgkoxMcQsb7ZkJyEnhmCEVgxqDFihrWtms4pJbcAaWlshVvjxj3/ge/uwvUtiBf4S1jqcrbI231mm0yliTWbqJZfzNHkKXzV16dmlEGsQY8TWFc7VYIRT5gwpjPh+STIWTZHKCrO1NaazKUYMvfdIFCrr8rKO5hEaZQnHWj2eYVf1lElbgzH4mMVCACHl8VvtbBbf+I5xDIzDgIiwNp2gjCAud8diaJqWumkyqYnwuR/6UV67+jpdF3jw8AGTUyf54NNXOHf6BItujt07oDWGV+7u8PEXP8lLr73JzTt3ePLcSc6e2uY7b91ib7lkb5FgNqPvFrSTCWuzDRbDHB89D3f2+G/+/m/iY0Q1EFVyYhCoEiQDDsGLpTYJI3kzD8htTYyMKaEpy6ZDVAwp8wYqBGMwqlBGnseugwJYU6oLwYpZgb/ECvwlBKWualQjFnPsXKvRE8YRZxzRJCSCpoAzLqvkrMUbxZbTum2mxOixKH4ZCdEjQGUNa+trbG1soQLjOGbZb4iAoZ1McFbwPuSVVueyUMe6rPizBmuyvmAcRrowcGL9FLURvM8lclIIw0CnHUEabNWQmnWwhqCGRRL8MpHmQh8GxvGQrh/ou44LT3yUsxef41d+9R8wqyv+zZ/8EcLikNui/MN//Ou0fkE1nfL88y/wj/7ZF/jE5fOsTyb80StvYJ3l2bMnmVUVv/vaWyyXHf0wcOHMGbY3tni4OGC2foa3d/ZxCskkBEs0Bkeilyw7dlZI1pDUITavLzuEiNATM6+SJLv0piyayrqfvLRjEZBERLBa+n+B5FOuECSxcqt/J1bgL6GaMCafitlQIg+NKuMyO261rIVGNGYlnWvyFl/XG2xKpORRzcB3VpC6oTEtTVNhjaWdTsAIwWdlvLVVVuZZi6tqGucYQ8TYOgtyKoc1FSKGMSj9GDlYjux2hkm9zTdv9HTjwLzzefe/H1iOgWEYWI4jcfCElEghQoz05JEeSUjHv3d2x2lEOJveRpNy6dQJXvjox/A+sPnyV/ncE+cZfWTY3qQSSxVGJrMJL79+nT4k+qAcLpacPHuK9188xe8vlww+sFx2XNg+zWK5xExPI9UaouQFaWOpSFgSIi6vHrucBKy1OFthXCH8NOGDoink5aR4lDRjZvSPSb2cCKwCGJIqlUY8pnALuhr1PRIr8JcQY8AIbTVBNS/XOLWk2pJ8IoaIJctgrbGoyVxA27RMphNiiEwmU1xdUduKxIQUoa5ralfnPhwhpryxNvpIFGFMiWFIdIeeoR857Hrm3T7LrmPZ9/TdwKLr6bvsOx+8JyS4cs7x+v1InA8EMWTLy3TkcEcUxRWGPInBiZCMIeFwJIIYKgRvAGNBhNFOQZUzF87QbJyku/c2rpry3PveS3QNa2fO8t/+4v/ChaalbmY8e/48p/d2+fDFE0wmLYMazu0+4PaDA+4tOhZ9x/xwyRgDfuyxIWsVVGPeXkwJryNVsnSS8n6BKEaUOmX2PyGYpHgSPglGfa6+UAJgSgJVV6PGlj9btJqCdRitqI4svZA8LlwFsAL/cYhATAGhpqoqfMxOMU3dohKRkLCmYjpZR2yFWovaCSM1pqpwa1t4U9FFGPpA10cWi57luE+3HDlc9sy7Bd1yZD6MDF1PGhMhjPQhQkrZFFMgJrAISSBKJrUQR55tW0D55ne+w3ve91luDLfACGoqBMWJ4IBgs4bfiAJZ1quSWxmrCXc8lFcUj0uR5GoS0MxO8t///M9z//5D7j58yDjkOf/7X3g/v/PllwjdnPM37vDMxfM8fvEcJ86e5MKZM7x15z4f+fCHadfW+e/+yW+gKDsP7mLqmvs3vsWDgwNAyu+T5/TF5Q91gjOCmApxjkEMlRVSLuYREhEICSQpSRImJbwqg6YsOy4LRyklxFRU7Yxq8yzrm+exk40sd15h/zhW4C9RteuIbUm2BVcTSQwxkcTQJ6GPgXGMDIvEQT9wOF/SL5Ycdj1j17HsRpYh3wyTGWolhYjGLExJmk+0pJJJLrXZjBYtDrSGZBRH9gSwRoqrTZ6jO3GM1lFpFtMEf4Intje4c+dOXpVNAzEFCAN9SoQ0gh+JcUDCSEwjKQQiERsDUWMx33xn8XVcm+Ks497OLs8+/yw37uwwP+zZne/x3DPPMZutE6PHVA2v3bzDm/fu4176DuuTmvOb2zz79BVOnDzFZz77Q/zj3/0jrt66wyCecycus37qMWYnEwEhYqklZa2CZgmyCIgz2T/QVtQYvM2pDoCkdCQkRUzI5h5RwaqSRHLbVQQ/MeUEEIeB5eFt7t97DVuvsXnuGabb579HT9i7L1bgL/HW3pSh9xwu77FYLtlfdgzLjq4PDH2+8mk8ArVmIw3V/PAaEqqWaDjaQ0NEiOJwRguQM7jFGCqyWk2KCi2RGfAq5LcaPRo8Q4wM6rFxQGNk1IgLnhQ9KYz8zq+/zCJkEUyluW1xrgIrOFNhjUOdYGuH2CkWSzKWCkFNbj8MghqDYqkM7Nx8lVMnttjfe8DQdRwe7vP4xUs8duk8ox8Yh562aVlbW2O5PGTwET/27B8uePXGDX7187/Pz/0nf51L505z/d4Oi8FzcmsbNRUQqDSLcvLrlBOgI+GNMFEICFWKjMV772h5YKmJSsvpLpGA4EhFyCN467CUisZq1j40E6abG6QEw3LJwe1vcXjnVeA//F4+au+aWIG/xK/82u+SUt5ST5pALQYlihSQCtHkfjkaB2Q1mmg2pFCpqMjmljYFIp6UAhoDffTgPTF1pOAJoUfDSIp5bGU0kgRMroGxJguFpFxkkceMjqltqe0Usdnb7nD3BucuPpWFrWowJptgOFWiGFK5RssoeBGMlsT0iP+9ljVZRyIYw2PPfIBP/chn+bt/9xdobEU/DmxtrzOfz3m4f8A4DNQmJz7rasI4QMgDd3GOsydOMGriPZfPY8Xy+W++AvUk6/6xWJMTXnb1yTv4WaRT4clEqjcWkpYloSwJtmXvP+U1wpx0pawVZNYPSqt0LACWnEyssTTrG5xfW2O+PPjzfbDexbECf4mQDMkIFYlKKoLJ6jKjoAQkKjb1BdBLfIhIGEmhJ/meGCMpFVCT+9FkoBKDGIc4lzfpXE01qXCuRVzLxNaoNWJHAh8AACAASURBVBjr8mafsaTilR8RqrLYkvK9WMdq9mgEN24ybSp8yDxAyowZwYAtY65MmSm2eNiZIpEzAJqLfpVUTDSE7See4//+jd/jzs07XH7icR57/DF2dw/pFgNf+8bX8GPPIAZMnlD0Q583A1Oito4PvucKZ09ucnI64+yP/0v80dU3sdUaVNn7fzQWV7j3qigRjWbAuyJyUmNQcdSiRBVqVQKaXw+XiGpRMeVeP8WFzGWko6UeSSiWqBRpcE51UQyTte3vxeP1rowV+EuM4y4m9ByGATOO+NQRw0gKIxo9R7fVWsmuOsZVuLImayc1tp7gzDbiDMYWE0+b9/CNgJqjl/qI4kpZvCKQoRiJZTXFlr+pyMurRw67R+LUYIQGYbZxBpMSo3G44mdhxBKKk20kC2CyTjGX2EkEQ8yGGWXxRcltyZUnn+TF9z7B3/k7fwsR4c6t24TosVWFRNjdy05Fi7SkaSYE74tXQZ6d9+PAi+97BgUOdx/y8ecmWCxu+wxo3g+oyEnVikVESSm/Io3k5JXTmKLGFvONRF/MR7Ih2dEKMkRpaG3CqcnkHwJ+QEPIOxWieAyigjGR1YUd3x0r8JdY3v1GsYtusK6iaSqknlGbzP5TbLGMqcBADXgBEUcEnMZ3tsyPxCVCBmU5gUIBtxdoUrnx5vi0qrBEim4tg0UUi83fX7LHXy6ZC1CaNbr9W1Rr53GaCTSjZMBRqgQj2JRJsWTyz2ZxREO29EZQ1/CX/uJPcvOVr/M//8LPceONq1x54km6rqNuG7bWt9h9uMPxrRiapxQA3o9Zf69ZJXnh3GleefUqf/93vsjXHijnPvA5Fv2A6FEbkp2KEzHbcpWe3osiRMaSHK34UpUUwQ4mX/oheqzxN0QCiWAsBoNxgmkbJmaKjiPDOOJSIojQ4BiPFoRWAazAfxyPve8jOLFlDAWRXHYbUSTlXnkUqJLmPjllo4rj2bGY8tZmhVkBtZCy1hzIUK9wJn8Pi+LNkZBFMWqyw3Rx4fFGcRoxanEIScqmm3nHy26/X7A5S0TrsMdil5BlxyXxBFt8bzITSTAZQJU1TGYz/srP/gws5/wP/8cvcX/3AYOP7C86ttdnPNzbp20bQgiEFEt1kW/I0aRoSsQUcLbJkwBjePv+Dide+Cz31k6QsHnKUV4PgxAKoJH8OxuUUQwVShRLhZJwxzad+fXJ/y6GXL3ksWdOqEcKBwnkVWUJuMqxtrHJMHbEISJJMRKQ1el/HCvwlzC2OoJqhpUarEQUi0gu0Q2QTFk8MQZ39FAXr7yCrTxfLz2tLSuqKlBJ/oiaPPLLwpyyjy6GKiV89LiQCbWjbTSD4g2IOpBc/ubVV4NpNyCNiG1QydvsTpUghiS5t7aa9QKopU5Z4NM0DT/4iU/w0z/yaZwm/vbP/S1u7+wwhhHEsLNzlxhOcPrUNsvBZ2WcKjGBEcVYS0gRjMFJRQiezY1N/uDr36RaW+cjH3mce3sHPNyfA/msLz7DVOT9fFdcf4IojkxMGpN3G2whIhVF1RRz4oRNmbuIZe6fJb1SiMEj/57A4D0pRVzTstEI/XyOSa4kjlXACvzHUQtEjqwgck2sYnBYVGwez5kMRlFTZvQmf115UC1H7HRxqi26dKsRq0pU8JJPIVQIFIMJEUyCYCxS5RVc73skJZQKNUe77YpIVdjv3K9vbpyhf3iL2amNbGslEKkQElbLrbUlSakoQRwf/OAL/NSLn+TC2TOoRu7c22U23c7EnclLRuojl0+dZB6FcVywmB8e+/+Vm7sLMBMhRNq2oW4qHj9zir/5i79EMC2f+dxPcvLKY1y9fhNDJvqO0p3BFGKT8ornRR81eQGH4t5rCykpR9MQLC7lfwMJWv4dMj8Sy+epZMGTJvDDgFYts60TLA/nSAx/3o/WuzZWNVCJqmmominVZELTNkwmMybtDNtW+R75usl22dYWiW8uvVOZPSdVfALRhMRcptqkmfU/GkGJlgc924OLmGzNLdnQkgIEjFK3be6JRcDkDUJslryasuiTLEgzZefB7XwhZfl48b5GjcHxDqFnyOvEf/Xf+FfZ3linrSz37j+k73o2traLJVgOQXjm1DrOCJuzdUAw1pWNOaUfelLMFl51na3Af/ZzH+fC6ZPcevs+927d4J/+8v9E2rvL049fOjbqcGJwxuaTXqTcLZB/VmNMFjlhsQgVxbzUgCuvhS1LT7Wz2MqUyUD5XsYgYrFYKslaBkEYvWfe90y21okp/ZnPwPdbrMBfIlqbNe651iSqEor7jk+JoImg5PGYluURkWy4aTKwUrkURqzL71uLseWBNQ4pDroYQy222G1ZrBUqC2IsYl2eh7ssc80uu9kqy4njyN5axFDjCsXvWB7u4cclGjySAjbXDIy5JS4EWU5CfhixovR9T7fsISWMrbhy+RnqKttpWZv7+hefOMPu7kM0xMyJSGlJipGplHsGfvpTH+XFD72P/fkCAWJK9P3Ab//q/8oLVy5hTa6Cjs7+WK7WqiUnKANEYzOfIkIweWSZE1nmBbISUI6v5aoqS1UJSN5NMJkKwKRI8oE09PQHh8zvvc2d117ilT/8ba596w++Nw/YuzBWZX+JpIIQMGpJkrtJK1q05UUCW8p+o4WRJ+vo86UT+XQVzeKViqrM1AttVbT0R7dR5f/D0RJO3mO3mp3qk8mfW9UVacwLOqrl+0tuE1SzG06FYe3UeXbvvInUjqpUJ5WtUVtlcxJrqMQhpkI97O3vYwA/WCR6ZtOWtpnwsY9+hpu33iieeZHdbuDKuZaPPXGJL7x8Nbc8Zb6ePfUSmxub/Nt/4cf55PufIWrkq9/6dk40Jl/J1S2X/Nr//r/xw//Kz/AP/9Ev4so0w5BPfFNGfCqWJBZnEsk6HFnqOxZi1CSLSL5SDDLxmpXTkW6IpLHPhh9jh44dwziSxp7gPYQBIVEZkz0SVwGswH8cThwQj8UueWwmRT5qqFVLT51NI/LY6mg1Jff8pvxdvlkuj/RsqSLAoJLydh2KxRAFRPPIy5VOOIlydGVFU9UMPpAf8TLiStlNN6WEaqBLiapdYz5cpzJTRh+KucWcAIghS39FjsH7y7/1W6ynkc9+5kdwFg4PD7E2sb21xZlzl7h+/TWmVcVi9Ox1He+7eBoXA7/18ussU08yFifKB9/zFP/uz/w0F8+fpR97YjD88atvMJlMGaLH9yPd2LO3c4ef+sTH+dv/5d/Il52I5OUcBSm38OojHuBiJDsFx1gqhKN/j3fsP6XczaXltE8hZlszoKkrqqbGrbU4JsShw1nFWpNbiVUAK/Afh7GmTMbTsfYcNTgyeJM5WqrJargs7c13x1kxxT8uS9SycUSpElL22c9W9QZJSi2ZrVaxhSzMY7sqRUK5zy4kjxphvvcQNGQL6zDmZaM0EmLCJU+MiWQNcexxbctRkkEFK4qJECULlI423n/787/F3/j3/iobm2u89NLLOGu4dPEst2/c4sWPfY7D3XtMBYbRMx8j7Ri4cuEUP11VvHr3Hl6ED12+wI/94KdYP7GVpcnWoT6x82CXum5p7ZTOLJgvF5Aib999yOntDbTcdBQBiWCsYFpHLUc793Lc2sQU6bWirlusza9VrRAkJ8OjsWwejyopBEY/gnU0TZvbqBhZ7u9ixmW2Ql91usexAn8JMSarxlTKGXt0q6tSFXGJpLyUMma9XPb1x+DLiOlIOqtFlGI0ETVkFjpkye+oAUIgpbF42fXEMGJ9hDiSomabrjAwxoCOI85V2HJrbZTM9HsRPLmyIAnJSN5zN+FYvx+NYNKRz32Rt1j4L/6j/5Qr5y/y9p0dXn7tLT7x4fdz9sxp1jdn9P2SH/jIp3nr218ixsjh0DFzFlM5TmzN+Nyp91CvTVmv8w08rm7w44B1joPlQ3wK+e6DeoKzjhgja5sneLB7gHUWrxEXbXZLMoqmfIORaS1WKrDpmBxs1FElpRewdUNrq7zEVCqh/FonTCrjh6pCmpqu7woVkr38mnbC6Lvi5LOKo1iBv4QFgmrp8I8WT7IFdaBcna0KMZEkl6wa4zuXVqTsmqMxX0EVQl7qGcOAhEAIEZLi8WgKoIkU8zAQ8mpqOcvQGBh8j6BU1QwlG3VGjioJRcWWsaMyimPSzPKii3VZzqoJTGbMHDAWvcGZSxd55vEnuH3nIQcLz/PPfYjtk6cYgnLh4gVu3LiJJuHC48+wc/ctlsuBRV1T1ZaJqzBWaG2+XUhjOL6kw6fEwwe7OGPou466qZk0a8zWRk6fvkiMPhOXmKI0zESmGEGj0veBdnJEnObNR8RgnNLEkTgKZuLemUioICliJd8wEEVIFhoryGDww0BT/BWNGMaoMESijn/+D9e7NFbgL7Fz+ypBsirOGYNaQ0wx31uvsRhIlp47RdCAV3Casr1XytdbR/Ih5FC8ClEDrsz1VfK2nStGnXLUrwpFOptv7fV+gJSgbrGVJflAKOSYSCbMLOZ4fu9EsJMpw/yQxk2zf8CRClCKOaYqrml44vKTDH1H3/fEmNje2mA5jAx9z2tvXGM6m3Dj5hKjNdunL9B3e8yHmo2mQtuKum6wVfbZj5odjsZhpI+RB3sHtMaSCHSLOU3lWN84weOPXcFZCCn3QDmZJUxpRWJKjJ2n63ua2qGVwYmjkqxqNGrwqWfsPW07JUlW8iWNjFGx0SMh0kWPxohfDvS+xyn4mEg+4KPPV5h/Lx+yd1mswF/i8OFdhhRgGKnbCWKyFVZSqDSvlTo95v0RAlYtoyhOobfQhlxiGy2OPOS+NpGJvYDLjrWUbTs9IvhMGTPC6AMYoW5niHVI1UDKvAISSGS/wKObaCLgUiI5S/ADLjVQiEnKz21dzWc//cP8hZ/8lzmxdZIvfu0lNpuaMSTuHyZEA4vlPjeuXaOygjHKYj6nqR0nNs8wjgf0g2fS1oRKsc7RVjVjCOzv7xJTZHfRsz+fM6kqJsXFeGd3hx/44A+iKdBMpzzx7Pt565Vvk0JfHIRyjotROex6losuVzdGykUeZf1YswJSCx+jqdwTmDQLqlLeGTDl907FK8GXOwHFWKqqLjcBr3r+o1iBv4SmhA4DzmYrK1I8PiVEE6n02Ue3wmTwZovpKJ4quqxY03fEOo5Msg/G0mhCjCkJJPe7Rg22yOUSFg0jooHatTib78WL5NVgk2K5Ei8bWRgNRM0PdxDKyDEP0Y4YdDGWD3zoB/jL/9rPcvbkab758hv83hdf4v7btzjcu8uHnn8fo0/s7+1xeLDP/t4OIIzDwDiMjH2HyBYfeOGT9Ls3WIw91QRSSKQall1H8BGfEnf3FhwsOryBzc0TjFHYatfY3trGx8DBIPy1/+A/5+rVb/EHX/gNvvXSVwjLjmgijVQwSWiI9DFiTdZHiOb13mJLACQ0kfkNsnOPSv5cYwymyivRMeV2yIg5FvocaR1W4t53QnRlagbACx9/UVWE1DTURYIqcgT0LK1NFDPM4z3xPNaLeTn+eJFGyomc9UIZkrUK4ci3TvW4OsjePwlSIvYD0UJbT0EK/+AsGuOxMk3KXXYWZRTJVthlFXY43MXWLVXdcOrCJf6dv/xv8cyTz3Dv3h7f/s5rXH/rTQ4PF3SLPfb3H3C4d5cPv/8DaFIePHxI3y8ZBs+ym9MtF4gIdd1y/vxjPPfBD3BqfcK69cTlHjosGYcui37Ecn+/Y5GU+/uBb75+jWG54PLlJzm5vc3Tz71As32ZycSxsXUClwLIkl/7lX/AKy9/DYnZinu5HJh3AdvUGGeKUWciYSGlDPjjxaqya6B5tCnGYBuHqWrw2eWXqHiNSNRs1FJahdvX31rlAFYn/3EYYzDtBMRQF916FMmz8qIxp5hHGVXCsT98wqbSu5bPsEXMMxYKT8nvm6IeVDEkE7OtVtmn1xjAwmSylsU4ZAOOfA2YKekkl7tJyKO8dOQyFAgquLphWC545v0f5D/76/8xDx4s+PbVm/huZDlfsPtwhxu3rhGWPdGANRO+/vK3mU1bJpVjGDr6YSTFfCX3GDxjiNy69SZqK154/gU2n3mGS6dPYPCkcUEICa+O83bCw3nPV3/xF+hHz2OXnmRSt4izNOunWSznvPXWfagsW5MJZ8+f5y/9lb/Gjbde4e/9vZ9n5+Z1YohIEuLo0SigUizTsoQa1WNn5aggMR2/JliDiZbKeUKZIKgPBO+LwCqivDNWXcXq5D+OD3zmR9U5KWRaQtWUhyWX0t5kKymrprjs6Hf59VEWbVIZA+Y+PzMEXoQ6PrKLnp/GnCA05ZGYH5FyxbcDOhFaUYLYUhXkPjkQcTFvuMUEmny+utt7VOCpZ1/gv/6b/xU3bz/k4d7/x96bxdp6nvd9v3f6vjXs4QwkDw8paiQlUZYoS2I8JLYVx2rd2Ghdw43dxI7dNAUaGEjnom6BXjXoTYBeBEWRuyJpGxipXaB1mjYeEI+QPImWLMsiJZKHPOSZ97zW+oZ3eHrxvN/aR7npVRwHWQ9BnsO9157WXs/7PsN/WHO+OucPfv8L3Lr1BjmrSaZHd+ixDOQ4MnQr+m6tyMM00m82xJRoZi3BeYIPzOZzbj7zPlzj+ez3fT9/5jMvq84BhiiQU+H/+sf/mF//p/+E6/MFBMdzN5+l2TtksfcMzbxhPYyAxcSB89UZewdLXv7Ux3numRv8t//df8OXv/BbigwEXW2izESSqvWa+vsoYhDJlxJq6JrWePVC1F+HVmYpjZD0wJCKzbh3553dzc8u+bfxqc9+TiZ7qAqypQ7jmRZyhkIWReMVKdgCoCu8xAThLZikGwJJmSyZXDIl6+NyKVWYUqW6beNYXr2BQW95Yw0UKM4xW+whrac/OWE8PiILlLpey6KItlISWd/Bt3/P5/g7f/t/4P7dI969e8yD4wf87m//NqkopXXdbUg54anT+tIxxhGJkaG7IKWRMY7q+jP2jEOH9575bEkTAteefJqwPESM41s+8Um+89u/g+vXnyClxDu33ub/+Ll/QLc5o99c8MGPvoyUzHL/CodXriPGMxQhOI9IJhhPHyM5rrh27YDv/77P8pN/7UdZnR2p+pBXSTL9DVRAsDFIzpczl4lfUH9HYgzOqsqSC6qBKFIY1+vtQV6ScOfdt3fJzy75t/HJ7/qsZBw+J5Joj63CM4mcC+RCRKW4RfT/EV3NldomICpugSiEFykKEaa+305Mf33BplJUqrqdYarxpIjgnMctF/jQEONA6UfW52c6lETbEJEK2jGXB9Rv/cbv4Z3l9tv3ufvwEV/+oz/GiOONW2+wOjsm5kim0PiZbgFyZMiJnCNl6BiykmHGcSSmTmcNOZGTsvFv3HiG5ZUneXj/XQ72r3LzuQ/z5777e7j99mv8we98gYuzE6QUmrbhz37vD/Hmm69z5fCQdrHPaCxNncZnU7DFKLEpWObeMZTIZz71EX7mP/8bgNTZRn3eigqRqIBIrhiMuk0pKn0u1ftPKgdj1s6xjUqXdWdr1V6UggUe3Lu3S352Pf82jt99R22ji2z3wQomKRQz3SxUHDrbEtTWNVTV6rq0lAaQTBKzFaXYesPr+aCjrCLIOELtZY0Ymr09SIlnnnme44d3Wa0f6GpLLqfVte1XhpvAtWeeZbmcc3J6RjcM/JNf+yUuHhzxnps3GfueTYo06OEkZsCZgCkKbnLGMoaGFkNyggTBWCGnEayjbQxJMqvVOc+976Pceed1Ls5PGb7xhzy4/Rrr1QkpR3IWfGhpbKBpPEWElAoMHfjAUAoeRySSMbSuZe4aHj58xHzvgE0EQsO4XuvItbbo02qPnGB7/NWneML4Y2qbpc/OiMGYVoeEkikpVfLQbtM/xS75a8QUt6w7pc5UAY0ykVDri84UEIsppQ7eakKqAP/2gKBi9idEvalQ4UnfD0QHg9bW1XPQFWH9fCEENsf3iJsLfNPQzlokl+mMqYeRMO0i/vKP/3VWqzXvvHOf9eqCT33w4/z8V/8hDx/c5qn3fBhKIVIUGCQFMZnGOhx6eNmqLYABGYGsJqQ2qhy5WpBBaGbMZ3OyGMi9mob2HTY4fDunne8zXyw4Pz0BnOrsZ8ERKcYwZkU7ZiP4BuIQuX/nLYq1fOKTn+Az3/M5fuMXfn6b3lOVU6SouEn92TGy9VSchnilnoxGDDFF7MBWhs2YWrFtj41d7JK/RowjMGnbT8o5tYysABOwl2YbRtGzE+THGMXpKCegaulNGn/G6BCxsgCLs5gs5JKw1iOulq8YbMmKMYiR4/U93WNXmbCcs3rZZdWxm3rd4uBHfvAHKUV4/Y23+IM//H0+9qFvYRg6To/v8fFPfienR8d6uNXtgTEgFrxtAcGVQi4NPgxYFwgxkKQgITPEkZx6yJH95ZzY9yoOMpvRDwO2agC0zQzvAhjHyekxzhRKyQT0YBHRmYeURBoG/HzO0Pds1ues+44vf/GL/Ps/9pP85i/8HBPIQqp4CKVU2rXOAKY01sXnhL/QvwsqriJxIE+10jQ/2OX+NnbJX8N5FYy0FQ0iU0IbNEvqTp6qgy9SASS1XL+8faYXW6k0VS3Xk2g9YUXI2erqqaiS7cSN3+rOp8iYCljBWo8VyGMmJS17c10XTrfjtRvvYR4CR0en3Lt3l9OTM1YXp6ScKTnx5NUrvGqyeg6KyokH6/XH8RZrHEGEQcAnT5aAtw6XMykUfDMjlj1id8G8bbGh0R4awbqGBsGEgAszvG8RFxjTACaQEVLJeNMAmWgEyYWYhUXb8OaDdxn7kZQit9+9xWYVMdZW4xSl7so21aUmN5dVWv1dWZkk2HRfI1JqmzQZldTzZNftb2OX/DW895cAnW01qftlU1IVx+Sy1IQtpVeXSqrbV6i3VQXyYOotLVJv/sc86qqspX4yMw0TMEXIZYCkA0VVqXF1qFjL/moKYsXw3/+tv02MkS+88gr3773LCx/4MBebDlIiFyE06vVXiigqUBLZWFrjwKgUuRgDWVWAvVPtQBUDVZZg01jMfEkBnr75Xs7OjrUN8Q5r0MPCt2qzbTytbRgqFgfJClkunmwLQ0mIhaadM3Y9Y9aW6+TRA37xV36ZvSvXOHv4gK3dxuPzju1wVYFWFFMHflI3NBUMlafLXhEYRuzlWnYXwI7cvI1hHBiGgb7vSUNPHDriMDCkyJgiMUZyTGp2mRMlR2IRSimUXMgCWSoCreQ6oKt96RagUmoy6BCw1D+pw0Mj07BRdNZgjOoD5kIuud591Kl2PSuc5zte+iQxJv7wS1/ilS//Hm3TkuJAKRnJhbOzM5xTg1CqSpATQ7FOZw6mgaIeA1aEYg0YVxl0yhFIKWKAs9WKJ24+x/7BE+wfPs1y75D54gquWeK8+hvorr6oak4pikkQVfxtm0arqqwiJkPsiGOPWFhfnPHGq1/lx//qT2P9ZOpRn+OiZqdFSn0e9X1StyoAzk8/j9RqanpuLxu5y1HhLnbJX6MkVccpIqRpwjz1mmjSVimPOtXXW2maL0/S3Y/3lZfJKnUwOE2wS3XgqQNB0UFgmZRpmAQ/wdoKCqqrR/PYPMIgvP9bPs6m6/md3/0ise9YzPYRMmPsFZ8gmXfv3qEJbRX01K9vJE1nEEbylqlopOCMV6kw4zFWOQu5FMah49GjBxzu7TGWgnGWRTOnDR7vG2UPWmVFYiCEtq5G1bYsNA3OOO2irMF5T9z0OpwrwthvWJ0e8b7nXmCxmONcNfUwBut0KDntN1X1VwUKq8g5CDgf8KEhhKYeXo9tA8xjG5dd7JJ/GzVBpwS+TEoqqIRtNk9pbh7rO5VZRp3A601ftsi/x7vWy3VhqQPE7c0kl6eGPkaFO8U8dsPV900Lq//iP/ovSTHxa7/163z1j/+Apg3kIpU6qwfavXvvsjef0RgVCytSpcVrZVImdZ1SMCgC0LmANw7jAiHMsH5G8p6LzUrbkTgwDmvSONRyuuIi6s8tRbiyt2BIPVJ05mFCi/eexjc0TYMgjGMEhJQzkiKpJL70lS+zPFjSBFcFPKuzEJMxqtnu+m19n6mmKcYYXAi4yuWncjCcYbvN2IXGLvlrqGilKChHgNrDT+/bWsYwHQiTC02uwzrqAWK2iWkmi+n6eS4TncsS9PEB9PR1ZGqW9cVqXC2B64SgbL8Hy3d+6pP86m/8puL1o3oFpTiqFoF1lFJ49PAdFosF2VcfARxRQERnENRS2opoFWAAF/BO5bKN9bROtw4mG1IRNTBNypMvSQVGbBZKtkjOWKBpWkiZGAdySrQhYHwAgdY3mCKMsQMxpDiQrIJwTh484MWX/gzzeYsLgWCrcImVrVCqtUYPkhBw3qm0ua+7gFz0ezAGa/V91jmtNnaU3m3sBn41DN+cuFLL/EmEQ/vHx4t7s73JtbJUJR6pAyr7+PsxXCIp61BPtC9VoM7lAaGPLvUr6AtVNenVtpO6zsIIBM8YE7/2m5/n9luvE4Jy+cc4qHJNvYFPz89o9hc092BjhYBH8ghZHYQFh/UWiRbDqB6CVqmzkgXKSJKssmRYnG90zynKsMvT+4zBJMG6pD15zoxlxI+O2LeIM8zCHIu2AIKtVYfClCkQUcnvv/hv/iW+8crnSbNCN0SMqM4idcVpreWpGze4fvU6dx7c5/z0eAu+yiltD9RAwVjVANQH7JJ/il3y1yhUgI2pQ7mavIYp8acps9muj5xAsa5iA6bkVQxA2b5FqwWV4LO1vJ8OFMu2xaAmfdXbQwxiBWQqeHW9N0EPjRg+/K0v83uv/AHnJ6d0faeilSKMw0gae5x1GAN917E/X2CcwWMwRYeNOQ34msjOGgaJtUTWMjq6BluSeuDp/hNnlUbcNDPi0ONA7TJLLattouSqhpQLEhO96ZDOU8bEol1im4D3mvhZBMkRU/K2vE+l8OS1G7jgmc0bxlwwGdpZWw1OwFnLet3R9XdJpdA0c9X+y0KymTFukoEKAQAAIABJREFUarVmcfOW0LTYxw73XezK/m0Y1IevVNOIiqDZ/iuVWPI4qEQ/TnTfX29wa1114tG1krGmuvKYreb+9PmMUcTdBBqa9AEMwqQfqsi0OoDMdbFY5wj/yd/4j/mlX/oVzs4f4V2V8yjC2I/EIjijpXFOkfligfcOUxwYS7YwlkKRVNWH6mVuHJiCN5ZgBWcbxDbb9SUFxtizd3AFsWqRFbMmcSlqjZ1zgQKbvgcDeRxJ48B6c0GxcHB4SIyRYRyRmHRLIgJMgKbM6dkK71Q2bBY8GEg51fWejj9yzIx9X2W79fnBGHAG64IeshgkKkV5mm/sQmOX/DUMKtNlsXqDTEOkaUZktrOn6gdnL+G45vLen84L/e9E4qn2n7VklbrWK8jW8w4pNcGm76gOA7Kh5EyWghSdSehDLO9/+gbv3LrNZrVCnNONlyRSVjgu1qrzjgjLpqX1AVwlHQG5VJ1C5/UAsKHCZ61WJsYqT97XSTlQiBydnnD14CpFhDGNmFxIZVTGYhmUyQj0fadYhhSJY89mtcJYx8HeFZwLHJ88IkuGXBsq6zHOE8Sy2nT4+QIXDLM24JyagFy7fp1mPsM4o+tAa9XgyyuJR6sYi/G+Gn46FfEYY9UM2939U+ySv4ax+oIXW8t8O5X3ppbeALK1h55K/0lYY9L2QybmnanbgKo8o0we7KQ1Vw+A6bETyGdbU1TQSq5Kv9OgcNoKtPsH/Nwv/CO6bs1spn20GEMuiVwSbfA4q+AbSqYJC9rZjMAEQFKLqyIGlwvOObxVVSCdNWodYkHZd75V3YFSODk+opnNsFVhJ+eIFJ0vFAGTE84ZxrGnZCGJYRg7NqszJEfmi32cszx6eAdyREQZjbaCmUpN2Bc+8TKNdbRNoPWOZtHyE3/px3n++Y9U+DQYo/ao1jkFajlDaBraEPA+6IFrgZIQawlh59gzxS75a0jd3evKru7lt6u8KfUvJwE1ZRVwU3TnrpN4feT0j97uMrXxdcJe31svd6mJrV+2TNPGWj6XS52AChkW4G/+9H/GnbsPWMcBE+YqMGIgpULXdQx51JvUOqQI3lna0BD8dLgYkIjFkEtUKzAytnoD2TySyZgCyXqM8Xjf0ljPOI70Q8+i1UpBclLGnQiQSFWvYN1dECWiEuiwurigX18wXyzJFM5PVPxTpGxLKuc9zls2fc+//YM/gqCDPhcc40XP3/2f/ye+8dpXySmRU9w+l6Wo21IWIXswIdC0La5plG9gLViwzexP8mX1pzp2yV+jUOG4Mq3kCtlMu/lJgONyG2fQRCzTlH77sfVgqASgS0pq2c7/t1tD+Ka3iZmgrEoNNpOjrJl2+9s+gxfe+yGG02P253tka7HW4wmkIox9z3qzqV546n6bUmTeNjjTYJ2aimb89lCxTmcVahRoEPGqIViZg94YnDM6NTcWJxlrPK0NOmdIkZwSKSZMTpSUcamQY0Ki4gj61HP34T1ccIRmzvnmnFIiRRT4bJ1HQqOmnmPk6aeeJceILYVFCOwtAqbvCDaxnFlm3uBtxnmUqpwLJhfKkKpHQu2cssp6xc2aFPs/0dfVn+bYTftrKEV3YvXVt01wGpnkty8n9YDaSsvE/J8MNB9b3G9pvfomX1dl9vG+GpiwqJOOkKADsGmpWJvZqgFoeOnbvotf+H//EW/dfpODazcIfU+ut58pqqq7Zxdsab/WcXF+QfAB4y1lNAQD2XqGKnAhRfH3XrLKken0j4Io/dZ5ckrqlOvVSts3DbYzYAoma8WQE+BaurFjI1lvaFR92CJsVhekYeD61ScZ+3U9GPXADLZRo1ED49izGRN7y32kH2hDYdkEupRp5gFrbVVGysRUGCVyvlrr8+oszjXKiygVci2Fkgq5XPzzfzH9SxK7m7+GqVM9qRO+KRH1zYoqU339KZXNFvij6Xop5LGVmt72/9OSzzERVbaIwvoYJry6VBPOihicpo2XjYThZ/7Tn+Hs+IymmTNvF4R2hqvw2phH+mHNMAw8jnm/f/QAH1qCrTZZ1uJdfYgUyBP5VSuWXMrWkgxjFLJrHcUIwTi6OLC3f4D1Xj2BRG/fVIQSR0qKbPoNRTI5F9IYGWJiszlndX6EawMlDpW9V1SiO6gEl3ENRTJDN/CBD38rfRwpQ53Y50weCyWrElJKQk6iqEarM484RDbdhtiPxFEQPNYsMH6JYfHP+ZX0L0/sbv4aYqoSboXY6Dpumovrn3VmD8Zue+9K1K19/TTAoyL02L6NUkUoRVV/jVz2/YasakFyKVI5FRCmtgPTMeNnC377C7/Fg/vv4JslaVxjJWOtx7iAGSNdGmnzsP14Zy1v3X6DT7z4SUCYi62AHOhLYuYssUwVhuoELzCk+n0GA9koMtBaS8wjFxdnHF5/St103aXMNsWRJFGcVwqyMUiKCA7JA+PmgqPzE+ahIaWqbYhTsJENNC7QWEcaExfdwA/80I/xu7/5y5RSSLkwZKHQYbDV/ViPVsQCra5LXcVgiGgrVRRQZasewi40dslfwxTZKvMUa6o2nKnwV6rEtnrllW9C/KmSLPq/2+Mjoy3E9A557E99v67tplZjahWmRGcSsWACFgEGfuzH/hq/+Cv/N+dnx8xnHbFzLBf7FFGwUQJsHEkpEU1dWxrLrXfe5JMf/xRtE+h6h81Cbw3kovvzEjHW6/eYDRJMBSs5RNKWKeddoMTMeuxpug2NdWxq4qaSUVVdaLzHjQMi1VSz6hmmceT00SPK/sF2P2+cYvWDsTgbcM5RcqIbIu9/9j0c9wabg1YsMk1JpopLqlFnoRiVV9eDfDrMUVFUAGu3moq72JX925jw+1IZdMXI5Q0sKAKtiIpylDrEK1CUa6q3TKFO58vlzU5dA4p57MCYmgW7Leh1ZlDrhkpcmXb6W3kqC3/hu/88m4sNSGYcOrq+46JbczBrMaI9f06JcUxVvsphsTx68K5y6kOrQzurbUuwgViEmHMFJWlrU0hgCtYo9RZrLk1AnccUS+7WuKah9Uqk8dbhzeXOvuSy5UyUEklFSClxdnykt3EeddhX4cQYgwstLniGcWCzXtP3Ede0RCOMOROzVgslZxUUKTpr2LZmxuCNY36w5DMvf5r9wyvaFgWHsxa3W/VtY5f8NRT9VUv9IpDLNrkntW39Sz0cZGLa6YpNh4J1N6B1PtO+XgE9lRYsddr/OBddpL5v2iSgCWNEDwgRBRXheP3N18F69RIsmRQT3eqCOHRcWe5pxSAwphHJmSIZaw1jN+AbT/BOB3+CEmacpYgjFzUFMdbgMATXqF5BPYhKxQWkypLL9fAKvsWEoKW/NZfwZGu0EqDotL0UqJiF9eac2XypwKV6IBrABg954MGDt7h7+xu8c+sNuq7jI5/41i1wylaUpbFUQJZV4lPw+OAJsznNcp/l3hWOVx3GWHzT4pzHWq+c410Au7J/G7mu5qapfn0rhsthG7AV+ETs5Y29pQPrKVDqksCaUleBmsSWywGgMvMmqKk68FIMxZZ640/8/XobV3jxu3fuqNOPa8hEShqI2fDo+AHP3nwvi/mci75DciRtv1r9/rMQgq9Jmii2QNI2psQRCQFbwFgHDmQYwTRMyqRO9YrIpiBpREpAPAQfsK4CajA4Ruah0cOncpCyUZ+CbAxlTDTeU6pbcTFeD8k4cP/tryCiZqBn5yd88APv4yd/4q/zX3/x9/RcsW4LPLIGxDi8FYrVDYCtWv7Dpude1+FFkYrWW1qvlc8uNHbJX2PbCVbyx+Pab1PxXpiw/YJKQykizj62ClSpr6LCE/XGVlfd6fM9NhQUYevTve1FK8TXmKqI47bpa6ynW68w3kIKWCkU48gp0vUbTs6O2N8/ZIiJkgspDYq3nzgJIoQQaIInNg0yJqCo9Vhx5JxxVtd8JRuCcwzjoHbfIpig6j4OQz9GBdYUD9bTOIdYyygRmxPJeHKOW29BkagSYkmIeeDR8UOVSAMa1JZrc3HE6uKkPi2GOPTceu2rfPb7fxDvvP7codHn29nLAal1+O2WRg9xb/XgbpwKpLiw5PrhgjaEP6FX1J/+2CX/NqZ984TRqwfAVu6mTsKNYfLcKwZs4ZLyO/EA5FLq21S7L5lGVFW/Tx9rKNXIY+L2eTG1/5Xt2aAMQctsb5/Vutd+d6o4JjRCypyeHtO2S1onrIeRFIdabstWsttZR2gcszyjlJ4hj7gsRCmkpFbfXnIt9RV7kHKmOK1eQpgDHdbPq3yxkIzgTUMm4RjJxpDjSMmZZBKu/lxZ0FZlHDg6PsL5gMmiQ8LUsx4z/TCAMbRBpcLfvPUaL9x9mXC4z7W9fVbdwNj3ipUQo4AjB84mGuOUkeh0/mCqpLKVQnEwazzO7QZ+U+ySv4YiTC/FNUvl2FsqNr8m+HaXXz+umFKn9mwPhe1oHrQHN4Bx9X8nnIDVIaCZ2oip16ceDHZ74FhjMcbx2c/9G0iXK2ytGmqWVHXtYIwDq9WZimjkkRz7rUVYyoWL8zVN8DQukFxWMAyWQqRYQ8wJbwSHJznd+5u6KsvF4ItBnFZGs3ZOkUxjG5rSM3qDEY8tgZRHSlJSj5RIsYGCR2RAimGMPcPmgufe/zxvv/51SomIOMU4ZMXpi9Gh6tHDY+7cfoub730f83FN6teEVkelpoqNBGuhIhmn35dF5ys5j8SkMmUPOhU13YXGbvpRQ8tyTdgyoWinpHxMbGOSipZpIm9UaspaV3felWlmtWx328dRaboWjKvc90syi7Me4xtcmBGaOc1syWx5hfnBkywPn2Hv4Bn+vR/9KSWpmArBvVwGbsFBF6tTUoFYIiKlIuAGRArvvPsGPljadoY1hoCq24gYgqADupIwThWAbGXngSFJoaREwlKKqh2HdkZoZ5X152h8wAcHriX1Pe21pxhTQnKEnJCs1Qc5s7445Tu+7XshR90KGAjW14l9bY9Kph82vHvrDX7g+36Ai9Mj5kHYnwXmradpHa1X7AGYal5aiOPIxabj7PiER/cf8vDeA+7cvcdbb9/h9Xfu/gt4df3pjN3NX8NO+b0ty02Vea5HQtXS81VKu+pw1MdfnqFbhh66ZpuEJnXfrmAWYy3OeMWyO4/DgfF1sOcuufOSq6ioGn+2bWAWWpz14AMiUgU51dCjFBXyiOOg6j9A162ISW/xb9z6Ot/ysZfwfoNFSTTWTsCiTC6JQktC8NYSi8VaSJKwxdV+2lCcg5Kwfs5iPuP8vGoYiMH4OTOXWG16Xvjgx3jl4X1SVgjxtAmQEhnGjtC0242HM4ZcClm00rBZZwbdZsX9e3f54Ec/RspRTUpzYcxqhJqqA1BKqf5dkYC5UDEHhfyYAIudABW72CX/NqyrJSOAGnLYUoU6BDDau2MqR3/i8xudbxtr8LYmc2XTeRsQ5zEmaF/uHDZnksDk1KuQ2EwmYer8YFoTbinBdXV2cnJM2wYkeEJpq/OvA4l1Xah89ZJGBENMIynqek2M4d6d27VqsMzmLdka4sYyAAkV/yhiMCXjQ8OYBW9Qv8G6dgySiUZXie1siWk81jYY6cnO4IolNy0xjTxx7RrGeUzJVbK8YIwhZUeMkQf37uCblpySbhGkkFPEOG07Uoz03ZrV6hiH4e3bd5CSFcpbsioalqqlKBP0R9WBnXHgGqz1BOtxzuFsA373kp9i90xMUc02rViMLeqI4+vtX1dI1qqUtbUea1R4wuI0qeu9qPv+AlL78TFRTNYEMAYpiSKmVhqixCAx1Zaq6GGTpZqDSu25DaZpePTwIe1sTuM8uVkgOTKmfrsdIBcSMKQRa4Sh73VGUclHfbdW6q4VQtPi4qDKObZgiuCKJdlEkRnGBKyDmFZVDUu3FzEPgPbtxjoQQxM8vTNVaajgxeoOfxzBeaREthJm6JCupMijRw958eOf4SuvfAGQyf9MoxSSCEO/4cG929x/eIcYc22nAiZ4gnNYrxZh+ntxGNfgqmJSqZNVk6MOP7MgJf6LeX39KYxd8teYzZaV1lox8s5rwuMppmL/s0pQU3tpUgGJlPgYG7AO8FQWu9TZXgXqTDd+Leu3qN7JWhqhFFOn7TANBjGFdrZgs16zt1zqCrEIzeyAnEZszrjiSBIpJRFjj7OWlBKyXSdq6ZviQHABEwrWKj03GNhYr8IaxlVkoFFtfyxSRl1jOsHRKnovZmIc6YYBV3XyY+4Yiz5XXgz3H9yl9YEYh0ukRBGMUc2/zfkxf/az389XXvm8AnecxVuPcarOIyLEFDm/OOO3v/CrPP3+j+svy5ntft9Yfe6jlO1K1RYYklqbD31HGgc1HZHHCFO72CX/FO3ySXWFQdF4JqmKjtDrus+glFlxlwQeKVhR/z6xdUwvBrGXzL0tp19UNFvR8pcg3+nAYAL1iBp6bh18zLR8zPRxYG9vCUZdbGgsfnFIKgnJBWscsQykMYJ35DwipspV18Hjm2+/zgff+yFWeYOvJqAYh0nTFkE3A4jgMSQRojFIlQK3UuXNgBRHynmkbea07ZySBBd75dEby/2je+xducLJ0EMe60+dQBqkCGNUURD15oPWt6z0qSDbgqRYTVFGju7eZnblCaXmGoMvOoNJRgjiUPvRDMUQU9KWYXVBPw564Egh8ziAaxe7aX+NblgT+440bshDrzryaaj2W0mTrd76RgQrin1HMoZJcScraq8KZOhqUKoDLxRRHHoRwRRUrEMEI7UvFyWpyAT5RbkGRgpDP/DCRz6iyjc5AhGbEt46QnuAdx4fAgZLHEdyrocIgvUO5xSg88Uv/o4SeIrgnKVpA9YHQvUFwBRMSQwVdz+t0IyzmJL0QKFgfcMwdFycPSSEluBnzJoG28yqOUZmHBMfffEl1DFDpbPLFjIdGYYN9x7cV3YggndOBVTq+1PSf621PPuBj5JE5xWu6BovkdW2exqMZuHi/JyHd97l9OgRQ99TckJECUcTLHsXGrvkr2FKRoiYUirstvbbon2qExWjUO6qSlaZrGVmEVEeXykUMi7lys3PbK02qgdfKfpCTZIvfecKlK2fnyBFve2UrKbfQ+42fOzDL3B2cqwth2RKGiBnvPXYdoYPDS40jGlU7b862W58gw+BEBrWqwuVum6DJn9oWTQNwXsMXrGMtlByqlVHwZmgBBrjlAEoCTuf02/OKAIhGKwD16oKT2Md2IDD8NqrX+EDz39UV5KTHJrRtmnMmeNHd/jsX/ghlRnX8koPvayrPmsty+UhvRSsqNKvEnlUHcGKIaEkn26z4uLoIbHvqslprWbkkpJdZCfgOcUu+aeQyYYjoyV4qiI+qb6INCkpqd4iepuUeqtIZfNRV3NSE1+qcSdS9ICRx8g81Km16NecDgvF9ReoenrKNMzYAienZ1o5SCblTCwjSAav1tnBBVJWeC+ogKcgzGd7zBdL/HzJ+dkJ1nlC02Cd1Ym8dUhtAqVy5XWK7shxJIqu4QoKKJrZyZSzYbO+0BvVOua+pWlavJthraNfb/Am8J73Pl+xiKA2yAVKZHNxwQfe8wEWiyXDuNFbvzoDW+tpmpannn0PRydHKnKSqTLdCal6AGOGuO45ffCAOMYqBZ5ruV8rqaLpP6kQ72KX/JchqgFXammqt0W+vHmlTgOk1Nyd6L5S+fz6gi4TMKaUeiDUg6De6qU+btpvbw+DUgU6p420VKfe7cfC0ckpKSZ12EW/hxx7cuxxSW9q55weYTlhjUp355xoZkuadknTLPjSV77MfD4jeE/bNnivDF9jrUqFC1g8ghD0elWwU064YpA4YBGuXn+a45MH3L79daw4rHUUr7LZofVKz3UNt2+9yhNPPcHB1SeoExKyGOUfDD23371Fu3+VPqr6kEXttZz3NM2cb3/5zzEOCWIiTVqBKRFTJKYeP24QJ7Teq6uPSBVMKuqNUCnXlba5ixq75K8hUvfGOW/37yaXyuMvWz045e5XSm8pUHn+GSHLZKNdD4IJIVgFOXULACJGX4yP83lEeQSTbkAdO1JXBGBhs1nrwYTu1sWqj93oFOFnS8Y4SzubkXPC+wbvG6zxxKGjCXMWYca9R/cIzYzZbIlxFkkFYywORdo565kMLpXSBGRFOuqBpofRrJnzzI1nybHHN47gPCF4xDmcdTgXaF1AbMNrX/0y3WZVkZH6vEkubPoV9+7e5VOf/k6GsVcglFPAk3WesNzn6vWbICOlCD4PhHTOLJ0T0imuP8aOJ5TVMUgkuCrUa4r+JkQ1CLecjV3yb2OX/DVKVcuV2qsrvz7XfJ+EIEsty1VuupCoHap+LHXNV6g44FIfnevnmMC4qdJ6q4tM/TgxE6eA7fBvcs8N8wUpTpp8gHME22DCHnO/xHqvDrYCjQ/6Mb7Bt1qG9936kkeQI3EcccHhvas4hmoo4sBsh33KPXACKtGVKEbboL6MZOCpZz7IBz74cSUNGcte2GPWzPBuRvAO4xyN98ScSaXi6nXnp89ATDw6vs/zH/wwJSoTrwKicc5x48ZTrLqOUIQsiWCFhdHJfiDhTanagANjxe37usJUyG8VUqGCsi75m//Kx27VN0WRx26FKs8F1bEnI9UzTwoUqyy5aZqv13dd5X2T/p7Uw6KCeiioDkD9O4Kp6HlBy1TMtO7j8mARx/tf+ChjP+KsPtb7trrWBmLsiWOnjy8TESfrzekbMEqW2Wwu2FsuadsZr339NT764os4hGANGwveeAhWq5Q4IHaGsQExAxaIRVSW2ylPP+WIOMvB9ffQDxukQmuXbUtnM2ILQaBPA83giTZpayCyraCywLA+5d3bt2nmM/qYAQVXOet4/sVPsn9wQLdZM5st2Tu4gSPjBCgD6z4ynJzgGyURSQErA1l0myLVA1D/U+nTuwB2N/8/E4VLdd1Lzn2ue/oilV4r5lLeSwRBRSkoUqsDqTOBqj8j6O1P7fHRDYJ+nm+eBWjvfykfomhj4RMfe4kYB4zzNIsly70DwnyJt56SBkqKCve1VnH2Vn3qrXH4doH1jrFfKTQWxzfefoPFYoFvWjXwNIYmOOZOIclZpF6e6iPobIDaDigzLzL2G3IaMdawnC+YtXN1OipGKwpjMI3HNAGc18OlYg70sIMsiTFGbr9zi8/9az+sfodTxWEDH3n+ReZ7e+QUSQC+QcIMmgU2HJL9EjEB41DexJZAZar82NZkDdnd+t8Uu+SvUS8jTdEtOk9vju3tPh0KUpTHXz9QmX56cGgi1wOAvJXGViav6gWYStoRLtdOWhnUcd+WFWjxztN4xwsf+ADD2BOcY97OaYIjp5GUO1IcEEm6izcW5wO+neOto2n3ML7FukblvUUpr1234XFj0MY3WAMuVECP8Tg8zgC2Vd0+q1ZaKQ/klBmHDbEbyHkEDCF4los9bHA01tM2C9pmzl4zwwadT4h1l8AmAZML4zhwevyQl77l5frc6zNjvcOEOQeHV/Q5cRUmZS2uHqi2aKVQjFcNAhVY10O1GqZOttwTS3MXGruyf4rH3FsvibKToq9cvtWYyxfQpMqj8Pztba4Jbh+TidY7vJIG9UWvBnJVkEIfN3EIJ4EwRAVDjRg+/PwL3Lv9Dt46WmO46NcM/QXBNhRjcGGGMRFIWKdJbq2nmc/JuWARis1YF1jMl+ANZJW0mzVzcumJ2WGNJTiHoHOALGpMklCefykDqRQMIzHN6fs1y4N9sLrW9LOWLFBypDUGl2BslzRhTRkjjUv0OWJypRwL2BRZXZzw2htf4+DJJ7h4dIQTz2y+JLiG/f0FWAiia0uKoRhtr7qSq9yZ1m25JLUhn5554+raVNsvs7v8t7G7+WsISgRRE0vd2UuZmvepJzeXyDsmxt30kEmhTx83vUeM1Ak3VaQDLc2nT2y3xwK2/qOHCIqvr9/de27eJKbIbLagbVtKUmOM0RSa9pDZ4irzxTUWyyuEMMNZx2y2YL5Y0rYLfDunne8xW+yx3D/kcP+Qk9Nj2tkCZ622G6ifn2tbFRApKoJRTCYW3fGXIrhJJjx2dLFjHHqsDfh2STbCYrGgmbfM5y3zWaOfM8xwIeC8w7pGRUirsEnJkX7seef2bf7qT/w0KenBsDy8ovONunXwIUzlFknUqEOygplEJmPUskUTTiIp1ILfTMIquwB2yb8NU3fr8NjFXi9lFZmUyvqrMl6Yb0pqQSp/pmyT3PLPWnxfOgJNQ62J/+/qrWRA7fIe+86MwHI+Y9bMCLO2YuzRF3gckNwhZagtg1YnKY008z0O9w9pF3NcaNg/uMrhwVWctciYefOtWyzmc7IVrFMRUedd3Zc7Yok6oRdF0xkDNujXF+tUj6/bsF519ONAGxqaMMN7TxsWKtPlHO18xmJvTmhagm1ovEKK7eSEXAplHDk5echytqcNVBGuXb2KMbC+WEHM+HaGNx5rclVPrhsYU+qBrQezd3bLxSiih4N1geCbKkO2C9iV/dsQCg67VfSZJKJ11V5dYbgc1k0JPIl3blfypk72bV0xbUv6Ovyb+lA/CXwYVNkHqHp+0/HjvFpVj32HQUgYLs7PeXSsuHVvDF13MX1hrAlEb6vduKVd7nHl2pPY0yMw0LYzENi/coW33vg6i70lRaRiBRzGe3wTaEyFNBQDuccZiyORxRJsYDC9qvJYBemsVyfYxZy9dk72lsbpENFSKCnSeMtycUAcRso44onk0pBLVIh0UR5/3Kz5w698iY+89DJvfe0rxL7HOsP9hw+xlVNRJJEzhFLopoMDi6Ih9Vm2xupcsVQTEjLWq85C2V3929jd/DWsUUEOay7lt6zTwZvYSdarTozN5PxSxT1MZbpZh3UW7xzBe1zjCL6l8S0+BBrf0oSW0DQ0s5aDw0OeuPYE1556hoPrN7h25UkWh1fZ27/KYrGkbRq80+8rlUKKI6fHR6zWZ/Rjx0jZcgVKTqTcI6nHSGLWLrh+9QpPPfEki8U+Rhyp2/DCp1/m0aP7rDcZv9iBAAAgAElEQVRnjEOi60fdWhjLbL7EWkcbPMH7Ko/lKGkyKxmIJWKtw5IpEhlzpB8u6FannG/WBDzFGJrGgW+YzffwzrGYtcwX+zSLJTiP9RbvZpV3r7d0jB0P79/lR3/4pzDWcnxyxGq94ujBPSb5MykTUUow2SA5X0qnTy7HysaohqimDjZtVWXO/z+vhH91Ynfz15iGc3ozq1ClM2GLRpt087Xc1z5UX0bVenuSqK4k1C1dV3R/P5l8SM6QRV1tc6LHUjhBsFsYsDLPCrZMswMYY6IJluOLE8owknKsvvMeX9d74BQ4k1UW6/rBNZbLBfPFHvP5KddufIhnX/g0r/zqL5FTZjV0BG8ZU1SQk7E4a7GhZRgTqU7dkwEjVR6LgqvJKtar/90Qid2GvusIzZy2bWjDHt46egqzZka0hbYJxPmScegwuRCdwaQBY0oFM2XO1+fceuctlleuMXYd54/uc+fBHdrZHOcc2SoKM2IgxbparXBndPJqSt62bFZUcdkYS/Bux+p7LHbJX2Pv4AqNs+AanFbiiJ8jcVRb6VGprEqx1ZXgtI23TA69OlgqBlxdARaqMIbR2YH6xunHUIRsdFUFUtV9jIJTjMF6W+cEcPv2XZaLBetelXvCfMmsWWA2HWExx9tAkUQ/DEjfIbKi36zZmy9YLmc8994P8ZHv/kFe/dofs+5WSBGGYVAH3ZiJMRJ8Zfo1gc4NRJewKSoy0HkYo3LoBRLg0qiHQ06M6ws2swUheIK7gkXYWy4QiSRvCcVB0UNssdgnjSPFF0zyW2QlJRPHNW+/8XX+yk/9TX7h5/8ep8cPaZZXeermc+QiKvWNw0rmovIxbM56BNcB7ISZKJSt+7LXsxGzu/i3sSv7a4TZDGMNJY+M40C/3tCdPWJzfsY4dOSS9baD7WrOGRTDbhWKGrzi2YPzOO9V4TbogEuHTQHvvVJsvceFGd63eBf0Y4NTy6kmVBqufg7vPf/Lz/49Dvf3MFgODp/gqRvv5clnniPsHXD9yadpmwV5TMRhzdCvSCVz691v4B18+IUX+Nbv/A6iabh7501KyhhrGIaO45Mzch5JOWFEGLsNiHCwt6BpFDwzpk6FNZyruoFq303dimAgxpHT82Muzs9ZrU65WJ9jWsUHOGvwsyWH165xsFiw2Dukbee6x/ezCi+eNiVwvjqlFOE//K/+Fnce3OX9z72fb335s4h3pLEj5pGYEqXbIDnpzKWqJhWkSqJNbZoeB9lZ3Q7slHy2sbv5a5wfP1JOuaV2iGqogRSdHmNVox+3VaMTq+W+u9zaqYMPplpJTQ0AOhtQOb4KDa4fL6BfsTYRYhSUxoQs0BfwK7//ef6Dv/KT7C8WeONY7B/ivee+a8mxcH76gK67oJSEtZammfPw/l1MsDz9xBP86h9+mb2nM6f374B3NGZGHAfeeOM1hjwyCw3zdq7qRVI4OFgSRbg4XWGKpeQeyUq2SWOnbQeeghCk8hf6NavTY4zNOAu+bVi2c8Z+wPsGZ1tmyyUpF/rlHmMc8a0geSAnlRD3xkEu3HrtK3zXn/8s3/69n+OJxXW+/uqr/Ot/8Uf42b//d4GOmAppHJQ9OVVhdU2rz13d/FdBEinKVcg7Ov82dslfwxod1FmjpbkA3qhWHHYyhNbH6U5eV3JbK2hRQIk1okozhiqfrQMqYw3FqDjohBtyoirBYgVTlBfgjKU6BnKJBjCQC+tNz7XDfU5Oz5GSSBlIPWergbFk7GyJx2KrIvD5yR2cwMX5Cc88eYVX3/4GeX3MfH4ApTCMG969cxfjCnvLPRo/Y7k3ZxgjCzEsZ3MIVvEPJmCtzj5cmFNiQmzGG0emYMRRYmSw59gzCK7B2Edc/9CLWBN0AyGFa1eeoORMjFeJccR0hk2OlHxBzpGcMvMmsLk45Rtf+xovfuLPcOfNW3z0pZe4efNp9p58kpN7d3HGEIuh5KirwaqpoBZnk0Cq1W2L82rmKeD9zqV3il3y15jNZpU04zCTiP+E5quckEn/bcLgmUpPtaKrtWkroGUsiosXpcVZwFpVmjO1NLXFUUyu7KFpaPiYD4AxOjuolcb/+r//b9y4dp3j4yNII84UWi84O2P55HOEEFh1a2TTserPiGOkW6+RUvjL/86/xd/5B/+Ut21D2NsDCm6YkfsOMZFujFxcnNO2DevcsZgvWCyWzNtJIEQUDyBgikOaGWMaEKApgnEVS18KQ+zZrM6RnFldrJjNZ7gKwV02h2opnjPd5kIBRs5S4sA4KjPPugbXznjz1T/m6tPv4dlnbvAPf+2XefetJ/jul7+Hn/vZ/5H9Gzdp5/uwmdMNF7gCxlus09/L0i+xxlHQNiWXTMmFIjv13il2yV8jBE/B4MRMBnyA5mUxtkJIBTFlGvFpgk8kUWP0ybQoFXeSrLLmUrk3V8RfhQAnlCWoV7g+xlZUIAqr0cdbbSn+6Cuv8PEf/nd5cP8OuSTG9YoryyUPHz3AhRm2mROcZ1USY79miD0nF2vy2RmvvXUPGc5ovKVpG7zxNG1LnC+RHBHJZOvox0gTAGsJTUNwjpIjylosVRPPVkMTS0K3FwHAOiVBZaEfOsQ7To4ecvPms2RfLbR9w9XDK6wNXFxcqHingbg8oM/H5GrMMZstWW/O6Y8f8e5Dx503X2X96D5PPHWDNCbSsMG3+7i5JbJH7lcYYykkTFTgT5aI5EQaOx2EVpTiLjR2yV/DVBJLsaLluEHx+RQF3xSYHDbN9mP0MSq0eTlwynWoVCYEWqUA6p8OJCtxxU7swDItBrd8c2MK2RTVBqikADcmrl+/inUNXbfm+OQhKcF6dUoIgY1vufHU05yUjs36nBgjzgcePbzLg4cnrFYPMCSsFJy3iPNY36iRZSo4Uyg24HyDt57gW7wPWp6LCm/mXPBGiBS8Mao96FA7bokUCfX9hsKa46P7LA/2mc/38U3AWc/B4TVKThxeu0o6Unmw5ZVrDH1HHntW45rn9pbsL+a8e/cOfXuVd259jXvOkcxLGOvojk85fKrFhzmzkkgxM8YRKapEZFzFWFazTm3ULnUNd7FL/m1YixJkRJi08g115baV9apbfEmXilDTrf6YVNelKZTe4qZcYstVoN7gRaG/Um28bLnE8k9OvrYKe5j6dawxLJd7pLjh6Ogh6/WKUuDKlavkVPj0p7+NmOCNt1/n7OwIYy2H+/sE/yxvfv2PYHVKQ0Zyh/FOS2VncdaTnCCpqPuOnzGMo+r6e4/LwlBGrNGfK2NwUioXYqoC9NAcS8bFzCAdrRTW3ZrTh/dIhwPWPoHbs+wt98CoJn/qVWl4z1p44iZH994i9h1X96+Sy8jb77zF1aeEZv+QJ566yYdffJmDxT6/+eu/yObkEftP3iAs9mmcp4k9Xd8RNwmqtXm2l24+sp2i7AJ2yb+NjNkSdcxjMl2p9tsKHtNk3r6E6m5fobVWqwYqSKAigMXUxJ8+yihC0NTptBirB46ra6n6eBFTITvVEKR+7qZtWZ2f0m02CMJyccDzz7/IB97/Ye4dHfP53/p/iP3AtSefod+smM9mhHCV3/m9z/PpT3+Gkztvk/KAk4YxQ+MdtnGUYihlYDabsZgtWMcB6yw5RQYiuSSc8dXMQ7DGI2VUoxOxiClV+9AQybTFIiWT+o6z8wsFFV0cce3aDfzN9zHf2+M6N0njwLrb4JuGvb1D0rCh7y5omobVqiMVw9e+/ArPf+TbWOzNODs95VMvfxdf+v1fZdOPXDx8wPzKVdxsQXB75LDAyil91+tBm43KoBpFAJqyg/dOsUv+GjojzvVmtxST1DEWUO8+Uf83VORCpCa0KIlEXWqkrvjsN7HKjFL0EFE4MNONXucKZTsXmG4o9HMWqZ9bLbKxgjENMQnGWJp2xjPPPsunPv0d/NFX/4hf+pX/k+vXnuJjL36aL33p83hrODq6h3eB1G24dfttnnv2PazWK/ox0mJwEilJ1LF3vqCdzVmtViQ7Yn3LZr1CclYbcmoH4rySj3LBOYOxlpyqWrChugYJhURcKyLw2sEej+7e4vbrr/LJT3831595htliyc33vp+379xl1i6Y7c340Mc+xTu3XuXw6iFvvvEqX3v9Gwxjx8Fszv7ePrdOj3jy6oL/j703D7bsus77fnvvM9x739T9ekADaGKeB4IgQYAEB3AARZEiKdHUaEmxJMfWkLJGu+JyEiexnNhSyi7HKZVLliJHckTJ1hAqpDgDIIl5JuaBINAAGj13v/kO5+whf6y1z71Q+Z9URIZ03cUC8Pr1feeed3nWXtO3vs8nBDHYNozXTmN7O/SXlun3FgjlHtgZMjp9khgTwbfKn0CH0Zjb3Plfb8bgIuAEviuzeHQ+n7oJfy76BVOusF7t1ie0V2gS1kNwkBeAkhF2HxsjsbtGJOGkTs2EISbv9evSUBKEYIqOx55/gaqsMXbI0tIyb3nz23nymSf50hc/xeVXXMvFF13Jww/dxXBnk8lkwic/+Xtcc811vPbaYdbOnOKKq65mcXmBYjxmMvSMJw34CbgKYxJnNjx+MmHf/n2Md7Y4efIkqZkQrMEhsNnSQGELgpPFGWkCgomBYCIhBGyKTCaSPW2dPsK+PaucOXOK8WjM4UPfwDdjzrv8alaWdrNv7x6OHz/BrtXd9MqKcPZ5DDfWuOfu29gZblOWJWF5iRTGHDv2GvfcfRcnT22xvNSjtAqLnowYtQ1Fb5t6YZHV1RVGNnH88Kv4GKaip2aOa8s2d341qyq0KQdmY2VElyMu0xVek5RfTyO80UYgSZn/olF9P53/ZwiPSntNx4eGaCpMkoUgcJlDG+NKoinBCB7AmxYTHPc88BCr/T7VcIerrriG4+trfPWOv+QdN3+A/tISz77wJGfOHKfxLcZY1jfW+MpdX6ZX9VgcLFD2aypXsbyyzOJgkUkzYtKOYbJDshWuKOkNllgeLPL444+ws3UG8u+EkHY6xcqnIOIhIXpC8FMac1XQGU1GtK2nV9esnTnGmfUzDOoeR4+9ws5ok1GYcOU1b+HaK9/Ia0c/DwRi8Bw8+yw++cnfZWPtNL1+j12rB9i7/wAXXXwxz730Eo9//UGSgY3NMVVlGdQCNzIGwmjIqJnQjHZY6C+y96w9HDtybL7O85+wufOrCTef1IZCtmGwYerARoNyyiM/wKRA6Pb7cxYAQWv+ZAuitdhUyBycgmQFP4gtZM8sQpuMLA+1LSG2hDiCdhsfW/AtIXmIHmLgzDHDB973ARYXlvnYhz/K7//Jn/LOd32QzZ0Rrz79dYbDHQyGqupROmHN3dpeY3Njg9Foh1OnT7Jr9x6qomR5eZkLzjufyXCbRokz66pHaoc8ceYYm5tnJKobIccIQRSJJj5QFJ5kCkgNofWElLCIBHer0tpgKFUp6OTJY1gSozBmsn6SjbVjHHvtJQZVwe5953LxhReztXGSuix5+eXDHD/2Kq6oSRh69YD+YJnB8irLe89i6+RRfCuipJOJxftIXTrqUpZ3rIEwHrLTTCjrmuWFPpvbDcmEOaB9xubOr5aUN99GSFYcISWDTPVFMdYo4i8ZhxHGSJxxgiSzxQwoyImuXUok72lDhHZE8EHn2A0+BaGy0gMgERUfkA+cjClUlGCiWyS66opreNM1b2L/WXux7TYvfPMQo8mE5D2TdkTbNvTrGlNU9KzFWcNJfxysJcbIxuYalSs5fuIILx76pqwNO0tpHK6s8ztS6my+rgeUVUnQbUMwNM1If1Xp9JeuYGdnmzObaxATdVUJ51+Ctm05c+aU0Iur9xXOUlU1D9//VVZ27cFYx9racc6cOsmJk0dxzlG4AmMcvcVd9Jb3EDy84+b387lP/QFFUeJjAGVEn7SJSeupi0hdO4wztCRs02CsxTkr16rd/1+P2HeczZ1fzbpF6dAbR7KFpLXG4qLVWj7IRCAkqSFDS/QTQmzwQcUzoxeNvCQKP0bngUnpJnKzIDt37JZZBAEYletf3Gn6WpK8RqnnefLZp/mBj32MQ4e+SV3CaDLETyY0raf1DYNBH1tUVFUfYwvKsmTiJ4x2tonWYI3DFQ7TWCbNBKoKkyzD8Y4cYF2zQTAIVVmwMFiiN1ggxUhZlKSYaINnPBkSvBBpTMZDkjHEEBgOp131aAwmRJVAV3QjhqIoOLN2kta3NG2LLuVz7tkHWF/fACPsQuP1ExS7V4g7Be+8/HI+NdrE2RLrCinVoiGa0HH6tQElM404C20rmn+90gmV99yAufN31lAp73xDiEPZYote0G2qHxdJ2BAVACTROApVbNdEnu0ld4o9+nUHEdI9eZTpJ+WfU39RPxfLcuD6fQx8+cuf5YKD5+L9iFdePcypU6dw1lKXJYP+gLqqia6gqvss9hcpmk3C1kkO74AxQtJhi1L8XDkKKlfibZsHlyQbVBPQEGJic3uTze1NMteYTUbl74IyB2VKLhQeLX0Tm+S1st4cqWzRiWWWZQnJ0EzGci0dyfkYKcqC2Da8d7flipVNrlo4wu5ijYv27OW/sTD0Y4pgMa7GGum/GCwpgU8tJsmWZWgTxEShegSjZt7tzzZ3frXtk99UB0tdmo3VBp81FFYiWkLWQiUBlkaYNMRSF63R7F/hQsqUo1+QpwHTXQGrl7FpytqbX0NH6yUHhVwm8ru//zssL69Q9RcIPjAOYy69+Bq5h2QIGAaDFfbXDdedfR4Plw1H1sR5ZYHJ6U0oCsGJFLeUNxCTYA2tFZIPM3N4CW2Z4h0sgIwejcl4iIxsSFjnIEEbpHwqikoOVRJlURFioqwqQsyippGtzU2MEZ6Amorr9i5w1dWXsn70CJNTh/mVj72Xf/bnt6ls+ghsiXElyQhvb0oRfCIYud+EoWkjmDQn85ixufOrWSuNIh88BNF3s9Eowk8HbzERlUIKNJ1NeRIg19EA1pFxGt0RkshuZqK4yX4HGjWLQsQyrDE4V1AWJbaQFVcKh7UyWjNAMImyrPHNmMbLPv6Nb3krh48eJSXLqG3Y26zBcJNb3vW9VIMed73wMltbE5wTDgKR45IDzrlS1HSiwJmJQXYXkpVNQzOVvBDEofDk5f14ea3LR0O31WiMlWanSoYbaylsRRta/dwtC4MVBosDDDAcDdm1vMyx48eo64rbTo64dXuH0LY89o1DXNq0/PSt7+Kf//lt8nknCLGF5HGuh7FFB7hKSRCa8jkL4s+5Ocgn29z51aKSQkTN0y1WA/oUHZYdOJN2TlP97BhWI2J2cRkAGDS9NvIHh8FYp9dKoBj/Xr9mMh5jMBjn6PX7QkzhW+WkF+KM6AO+nTBOW0LMoWPKYEouuvASmq3T7GnXcVsF/+v9z/Kru1a5/ODZ/MwH3s1vfforEhmdEJCkqPp/1mGtJa8tAV2TE5uUWNSIICi5/yC/r0kJiyPZkPFL0rswUuM7V4JtNdOJlFWNH0tZIWQfEPwE6yqGO9uCkgTa1jNJULiC7Ungnz7yKj/fRlbOPiSl00w9ZBKENMEUieQqSCLqkRF9gtPIudjcYO78na3s2S+ju4jq3Xl8DAQfwBlKHG1ouwc7hSiSVl1qmVN7vWDKOACZHMhUgI5e2gQRlUyZaILEcBQI3gMW07Z433ZQYKNbgrlGL4xwAbShxdoSmxKf//IX+Ic/8XFueOe7GZ05wZ2338bQR26750G+5x03UdpF/uj2Bzi5tSXCFsYIfh9B6dlugQmBKKMoR+NkRJnk95JJhBCWRGuIMQpNFo5kWl1Jziq5orRTWCfafhGqsmbSNAA4V+BcwjnLnj27GI63IXq5F1cymbRMQuDr3/gGGz7xxZ0B7vgYyoLUCPuQtlGAgPcTXEoYV4H+/2KBZDyBrN47N5gfg531llboL62wsGsXS6u7WFndxe49e1g9az+rq/tZWl2lKER0wqkKbHbEpKl75pALqs4bgxd9v5gIIRBC0DpVTwjpuHWyXM4W1FWPfq+m3xtQ1TW9qqJX9amqmqoW5t+yrHBlSQwtVVlTONGlP3XqODe9/SaW9u1jYXUvS0uLlAb+59/9AxbPOp+rbv0obz14FhgrRNdGaL5FF8+JJFf+vaxMOawVVuLCSOx3SZePlNfcabmENtysnTkBk9FRpghuWGNIKeCKWj7DBGVRAhBSIqTEL/z8z/P2ZccbKkeaiBbB+sRzxzOHaNuWjfXTfOHEkH/8G/8C4xxJR7JZWDXFiPcTUmhwxlJVjsWVRUorZB7zmn9qc+fPFhtiiITJmHY8YjgeMR5uM9rcYHPjNJtrp5lMxrTjCRPfCutMjMrGq1x2iD+X1gl9dylqNXWvptev6fV79Pt9FvoD+gsDBoMBC70BvUGfXq9HXdfUVY2rKspSruGMwzihFrcKE4aEbydCuFlWFEVJUVRcePAAT7z4HJOdLVbOu4jVfftwzjJuA//n//0Zzmyc5ulXD1NVlWQ36uNYqd+tc+AkIyiMwRmLNQXWCINRzGgn47C6zSdwqEIbfknXoJNqHmjXn0RRiGhoVNlx5wrJDWwha8/Bs3Z6g6eeeJyz33kLB5b6vH3vAiRYnzTcd2bMwV2L9FLDo3d+hacee4j/6td+ibwLIYrJRjUXDY2fEPyYGIR6rdcfaDkzr/mzzdN+tdNHj6gcV+iad/IAa3qMxZUOkqE0QFF2dbwxKEOv6Tr0RhfxU9Ll0tz0S4bpkr4s/5quefb6uX7UZlpqg0ZR+V8MnroqwUjkMwaqqs9F+5e584FHuGZ1D+78Jd5w4eW87eor2K4X+Rf/5nf493/8H3h5fYuzzj7I+sa6psyS9scopYWzrgMdGeuE/spIDyJj92W9SUjLbW5YGqnB86Zili9PCINOWdZYV4ost28py4pJM8GkKL2MBD5MePKJp7n2J36MLz34OLuGm5SF51PHRuyyhusP7ubTL50mhcSdX76Nn//FX+S8yy/llee+oZHfaCNGsqvWj8EYtrfWBX+gKM65ic0jfzZjcFaUZvtlRb+qqXt9evpPv+4xqHv06pq66lNpWl5VlZYDhTD5aqqcci1tMjIPGQcmgaSlGIkh4H2gDS2Nb2l8Q9s0+KahaVp829C2Da1vmYSGpmlofYOzjhjBN57Wt4QYGCwv8dzLx3j6G68wGQ95+YmH2X3WWVxy0YVc/+brmTRjXjl2kmQsVVEryYiQhTpjCTEKgSnTpqREfs0KrHCUCadBFsbIzdHUbSgmkjQxZ6YDIQqpSVlIrPGhxZWlQqaTsCInYQna2Njgc5/9HD/7c3+bwy1gDccaQyxqbj+8jXUlrigZbu3w6U99iv/6H/4jbFXpHQkjEkmWpGJM+GaC954YFGlh5pE/29z51Rb6AwZ9cfSqqiirShzaOenMW5mJ2xxdkpf6MQRCCiJoETzBt7JC2nqib/GtZxJa2kacOLQNbdvKiC47d9vStoqR195ASj4v+GGMlcUZHe8lYwWA46Tx1x8s0IzHHD6xxXBzm+A9X/3cZ6iMY49LfOlzn6NtG7yfYJ1jZ7zTpf0gDbkUhTxUTOf/XVbjkGNBGIhNMh0dlsRZnZNkYdMMdMivSIEYGoyOGFOKGFcKUWqSDcCkB0CI8MrLh7nr7ru59UMfECAQhmPeMI4Bi6GshAr9my+8yFe/fAd/71d/BWfNjAz61EIKhDCWXX50JDk3YO78ncWUiOrEPkaCF4Wa2AaCb/E+EFqP9y1t24qjNvK1b3OUbml8oG0aJk3DpPX4tiW0LT60+ODx0RNTFNYgawTDXpRUZUVd15S9vmYYA3p1j6o3oK57pBjoVX2cFRFNZ0uszrQXBiuMtzYZTUZ846WXaXZ2OHriNHc9/BBvvfYqis01nC1wrmTX8grRe6UOE0+QBp5IlRnQRqRoCSajDU0rhCXGCO2WUSePPmBwBGM6Dv8O6JBEXTeS8FH6Itbm7cgk2nlG0v6Ygo7vREDksYce57xzD7L7wB5poIZITEhsj9JDcMZy791fIwFvuvEGjFUehZSEmUFVgAW74TEGCjtP+7PNnV9NIrKm0W12akmzvW9pvX7dtkI/1Ta0oaWNrQp6TME/VhdJKuekeVfV9Mseda9HXS9S1wN6VZ+y6ol2n3bsjSIKgxEAUTLCm9dMGuHTqwq9tgh8uMJR1CWNH8sKr7W0IfKv/uwLXHHufv773/l3XHL9TfzYLW8nxkjd67OwtNzV2ApjxKrWQLe+CDPAJZ3x55l/PjBUEtuHRngPYiKqjlHOB1JeX07C8pNiEHBQTMIM5JzAcYPHB9mLSHpIbG5u8qUvfpG/87f/S5mkxDDdcdCuvTWO8XDCPXfcwU/+1E+xsLTcUYpN9fvkM23bBmsSYS7U2dnc+dW8puvey4MYtU6Nyq5jQYQ4y4KqLCnrHnVVU9V9ql6futen7g2oej2KuqasehRlTVEWOFdhna7ygurSac0flFI6yoPqrGyzFc7iXElhHZN2RL83wBpHYY1KWwMYqqpia3MT61QdqKz56uPPctRXfO/Vl/GZe+7lPe+4mcXCsri0QuNlVyFDDyW7zxBip1fNsONMJZ4U52+nuGUCWaBMGImVI08li013uEgJkOK07g7RE1svFOcYFnfvoihKOTKUfz8Fz6EXD3HvA/fzkU98DKO9BpsS0UTZD4gJax2HXz7MHbd/hV/9+3+fqld3GU23HJV0/BojaT7m72zu/GplUVHoqK3M6Xa/R6/Xo1eLc1d1n7KuqcoepqqwRTkdx2XSzpSDXSKmSApJAD86H8dBYQ2ulLq1KEWqq9D+grUykBJHiITQ4qyITqQoQtRRnTGlJFHboPdi5ZAxht/+zB0sLO3if/vf/x29gxdxzb5VenVPxDbIuwIziX8Sye3XD8MkegcF0SSrP5diJ4zZcRkrNj+fDtIQ1E2Ajr1YgT8xEX3Q6sDwsz/zc3ziR36Um256B6t79yurccK3nkfuvZ8D+87mwIVvAO0LpCzrHQMhyjbfo3oesbQAACAASURBVA88yKOPPMQ73/9eirJkyoOM7lGIPFmae39nc+dXK6uKoqwoy5784yylLbCuEOpnoxExilMa9XQRg9ZU31pcYSlsQVE4itJRupLSWRHddA5jZUdd5MA0MpqZZSIFCAXxfkbjEb26JwdIXl5XH4tExl7S59IKBh/lIPAp8H/ccT/vu/4mfu1/+k3MYEDbNiJxzbTpbawoFBljsFGQfuIfEv0zr0BUXT5J7pHDQlGKkhlEHbZlcLNsLgptlrL+atoeYsJHT9NKSbW8axeXX3Yl7/vgB/kvfvKnuPXDH2XvgQMYY9ha3+COL32eX/g7f5eiKvQelDFIqDlFLLRpePXFl/jYRz/Gnv37cUWp0GOk1CAqcnMu2pFt7vxqxhrh7TNJKL2clcUd4yQZtuIYzjhsYSmR5RtjBSWHnTplBp6IO2hKnAAsTvDDpBi1cy5ZQlKsekxRZu46RQjRy73EJOhA0aYSYg0B/HdTgewUKUrkjCFy96GT3PvMC5xyi0JGYnWvQCO/0zl/SgmvvAMZhpxTdqt/nzLxoGYyXvcCprLi+t+Uj8UZtLP+W6TKVcsgKHdBTDItaT29Xs0bzj3ILe94N2+9+R3UCwO++Y0X+PLtt/Njf+tvSS8iScmSktHPL+J95OjJNW677TZ++Zd/mTqn/2Y6kpTFqnnDL9vc+dUiGZQiSDEThW9fUk1D1P31qA9uJCqoBXVEeX1eS01RdORjitrFTsTQ4gndXDvEKGPCmHTUlWfoAInQNFRF1dGGR5WdzhBhWXvNAp9My4EYZKU1BlyUjcVe1ZcSIWlDTKG9kQyKQZuAptvPz3PxFLXGzk40A6XtUAwpKxEl6fIbyQMyZz6z6X/UQypFXK+SHkAIRO8ZTSY88ujDvPzKIfbvXeUD3/Mh9p59Ng/cex8OuOiKi3HGdkOF4IMgLUmE4Hn4wUc49NoR3vc978eVVde/MEn3L+Ygn87mzq82q+qaHbyLVppum5jn2SLnFWPQdFjrWGSVNT/vETkwfK53U5LaeDZJADJtVkoRE5LU1BHG7Rjjyu5nItNENyZZjw25txA9KWizjIhPQVNxBIjjKkLMcmDTqj6P7DKGwRpF7ZH1BrTMIXfRhc/PZAJTTPe7QwQTMSZilZ0IUPKTqS5CTIGmmRCCZ2FhkYn3tF5IQI8ePcELzz7N8aNHOfraEcJ4xHvfdQsHzjnI7V/6Eh//gU9Q1oXyLEopYqL2AVKkHU+456tf4fobb+bAOefo/4NJoMlJ+i9zE5s7v5pJQqctZB2REGV91WtXPqYggyytuWM+DKIAVGJ+EDWtl/RXACwkMHFK5pUQmi+iLPoEjYI5wnbd6RAwdopBSDEIp4CmzDFEqbdj0AZg7CYJKXe3C3Qf3xKjB+d0tKcHko7vcmTP3X9rnETvFEFHfZLO6wJz/jplvF8+0abiI/rBzvxX5h15iclYy76z9jFpW3zTMhk3PPrw/Zw8cZIjx45x5LXDnDx1ks2NNW5805spipo7v3YHP/MzPy0r1oZOQCUPGUmJMydP8cCdd/Jzf/dn6fX6WGO45qprePct7+b6G9767X2wvoNtju1Xy1JbJuUHuyXmUvF1D7tEwKgPvBDZGCW7SFp/p471N5KJQQ3RtrhkVBhEGnvJWJHqUmkwcgodPM6Vck/6/nmABagz5RHcdISVTFRcvXTDS1tQV32GTUNV15iihBQZD43uJmhkzj0FLIU1NFFITYxTx8+fkZxkRMDltoBhSmoiN6clBFoC5M9EyoHCliJEkhKj0RjfNoRJy8bmOs8/9wxt27C2tkb0rUw0rGXQ6/HBW2/lC3fcwdr6GpddeTnfePo5jBXqsATCgGwhBMMzzzzNWeecxff/wPfzl5/5NOcePJ+P/40fhGYe+bPNI79ajNIgC2lap2dH7MZUmmLHkOv6pBh5HT0lhfqmpBE9dvV8xGtVkboIb8ipcMJLfq1baYJKc7bAhMz/pz0FUge7jWmqE5DXfpD2g+4PCNa9XlwkkbBO9v6dLuugoz6rAB6r5YAPgdY3qDIhICO5TmjEmI7BJzt6rqn1O9pc01IBFPuvUuVlqZRfAT9pCJOGyWjEY489wXA4JCYhB13b2OTosaOMmzG33HILV1x7Le+46Z088thjvOdd76E36Emz1RkhSNGDO8ZAaDwP3Hs/F1xyET/6gz/K8ROv8T/803/M7/z+737Ln6XvFps7v1qGl0oElCZe3kyLuabURl4gdAdCjApM0cwhO1/nrEnq7Iid7pTkdHWm92Q7sAyQEiG0WGsIZhatlok/VXqq66NFLQ2yInDssofJZEJZ9okxUZqC8c62kGqgYCGrhBdW9gVa35BiwGG0dMmrylbvQw6tmJL2CablALm2JmdMmdIrdQeNsYbSVXIAOcfi4jLj0ZjN7W02Tx/DWWEXkrIkYcuSt7/7vWyOAs8+903qwQIXXXI1dz9wFz/2Yz/GdMUodeUSyRB8y+bGJnd99U6ueuPV7N9/LgWWRx++71v/MH2X2Nz5s0VJbwX5GpT4QUklibnzRZ7HJ7RHoLReEnXzWCkJYy2Q++lWk4g8Qs81etJrSjaQHVl2DECickSw6oQ0zTZyxpECHmEVkjFhwMeAD56EYTjcIaXEZLjJyE8QlzQ4aylcKVlEHuvl2t7IJp5AfpXUM39MyEFA53LTUgX9bKLJzN85szD6KUiN0BsMWNy9i717z+LAOecwGg45deokq7v3cPGFFyifouXg+efxa7/0D9i39yyOnzwlhZaB1X1nkejx2muHufZNb8rCx9LYi1kWXbKAl158iXvuu4fde5Y5/NqrzEd9U5s7f2cqwZ3b4yAdfQXiJNCVMIPT3paMAuPUofOUQGHBkTy/z/8EHekFSZtjJMbpJl/w8nX0gZAS3nsa38r3G1n59Xk7METZP9BdBO9bYhtlAcn7jjRjfeOE1PijbYyxFEUPaxz1YIGq7lGWFcYVWFswHG3R+laReZreZ6c1msAbdPVXBUa6qYjVpF5m77NJ/2zmkqyhXBww6C9iy5JeXbMzHnP61AmG4yGnTp/GOsMNN93If/uP/jt6y4ts72xLXqFYCGMsl196NY8+9hjXXHklCytLIpCq2VqMUZmVPT4EHnn0cc7av5c3X38dZVl9W5+q72SbN/xmLGFwUVLXHAuNAnSMpuTJGEHfaTNv2hcAMKpqk38+EZDZX6AFlfaKaSb+dAAguYYxgoQzKW/Pme598lTddg09JA036Ijy9cAaUmJz4zS7V/ZpezIR44TWe6L3NM1E6uMYaduGGBqqqo9VpluM0VreE5NIbptooLAKtrFkzD2giDpH1hpIRu/VyEFCSpL2lzXjrTPUgwUAhqMdNs6s8crhl/Ex8MM//jf5vg9+hBPra0wmDcSMK0iMx0OOHz1CCA1nnXUuDz54Hz/yN3+C3/vt38bG0PEqphhJpqBwMB6OeeyRR/nED32Cs697z7f0GfpusrnzZ9N6VqEsMrbLwBCibIPlgwBmXpu6Trz02ZJ2v+lKBJg6eDBGE+bYlQmJzAis0ZTcJhOn6jg1uw46zLg48a9+1VHoRtqJp/VjChLNcJtmPGI0GRG0k164QrYSQ4sxjqoULv1uKQa6PkJKErlD8N3nEjOcN//OJoq8mf503qoTUJIsBpmYsKagKCsW64pHHn2Wl196geU9q/zKL/0yb7rmjbx69DjBh26ZZ7wz5Pnnn+H5Z56i6Ne88fIruPDaa/nkf/gjznvhWW561zu5/6tfxYekqsqRQKIwIjtWrh7g6UOHueH6t/91PjXf1TZ3frXWt9OGG3RdfqHoFofOXmi0KRfNdLglzmC6w0AiX3YIpg6pICJp+tGN0QQHn7HoM/cwY923MTpyDN1fpPwW0miQ78UE1hDbhvFkRIiBZjxi3LYYY6iKksIVXQOwdCKq6cfD7j2Nli5GIcjSvJzSkOXX0NX/Odm3vI5YQ1eUTYK2HbO5sY4pLBQlB95wAQfPO5df/NlfYO/qKocOH6ZtW1JI+GHDM88/zX333M1rh1+lX9ec84aDGANLSwus7j/AIw89zAe+50M8vWc3GyfPdJ8RMeKT5cKLLuH6d32CU+vbHH7+QeAT/28fj/8sbe78at6LqMS0UtWZvpmN4CDy27IKa5PM82cDcgd2QVdgSTMZRIYRT1cBxCTiyxJNnMkuplFfcAQ6IdA0vwPeKHGmwnGkV2EE3lv1B3jvaduGyXhECAEDFIWsJocg5ULhLL3eAOuszvvznUGO8dGACUnQdSaPAaMejlLl56Xg6XGIlAHK/huB0dYGW5unWVgc8OxzT/I//pN/zt6VRdp2zJEjRxkNx4yHYw6//BL3PXA/Dz/4AME3WOvYv28fzXhEMx5TlAXXXXUNt3/5Czz04P285/0f4tP/8Y+kHNLSa//ZB3njez7OE4eO0LSBsOH/uh6Z73qbN/zUUo5wTKOvuOwU997V5ujmHVpn5xo+1+/d66exOo8CpZxISug5W/vP/HxngogzCj7qCCgVG9B1F6JhdiZPQvj3SSwsrHQ0Ya3KjRnnZFPQWNrosUBZ1NS1cN3HfMJ0JYqSZyBIQGdmPhumAT4Te+Z7l0Ntdhgn1k4awPK+d7+L0ASOnThC8A1raxucPr3Gy6+8zFe+9hXuv/dumo01oUUzhuXdK3zsI9/P5edewJEjR3FYLr7wfGKCV195hSOHnuPciy6XaYtzVMv7aOtVvvTZP2Pz8LOEU4fY2ZkLdWabR341IYlQZzJmWjZ3E2sd8elfzApziiBnRrXl3GGmXk9dLqGekg8aukieKbmToHe6nxZGHYm8Et2zk2ccguQTZiZLkNuW+1ys+5w6c4LJZALKMDToL+CsY9JMSAhxZ1mWFGVJM5lIfW9mVAL1cLNYmugpKJGiw+hrdN5vQAXNO0fvOgcpTwHku1VZYQsPvuX5p57knNVVvvnNb3Lvvffy9LNP0wx3AHDOYgvHYNDn+z7yUQ5eeBF3PXA3lbN437B71wqmX0PjeeaZ57j4yqs4/toCjZ+wumcfm8dfJNiSt779Q9SLC0wmc+fPNnd+tTTzr25Xf2YF9HXLoIr7/atLqxnQkwUkcjGeM3VihvDSNQQM00tMMfNTYY8ZugzEtabbe6bD3k/RdbF7ZznEgm9pmzGJSF31WOwPsK6kaSfEmGSN2TrKspYtP93bF3fuWpNSsljYd+Bstjc2SX66sSe/hTIVmSkEemrdio90Bp1j1+7dLNQV5aDmsosv4+4H7uezn/88Tz3yMFZptjOisrCW7731w+zadzZHjh2l6i2wf3WZPfv3YnAsLSyy06wxGY5YO3qY5XMPsnb0Va676hK+evtLNCng6krKm2o+6ss2T/vVcoSje6Cn9XnSOiB3rTMMNo8CTcpz8dkufe5ym5lDImkUjdPygtwP0LCYRC2vqx6U6GMqOGE7GKscQDCF1GQ3FLPGymKSot4WBgOqukeMUUgtjagFlUVJURQkwMegZ9s0Vc/vYa3lZ3/hH1CVFaZ7Rd4GlIpetgb1BvJaL2CMbEPYlHC2ZHnPXnbv2cP3f+TjPPPC89z7wANsbqyzsLICzhFVzcgUJW84/wI22wkvHXqRV44e45xz38BlV13HJZdezcHzL2Sxv4DB4FzJ8dNbrBjPYq/Hpz/1p4x2thgsLmOSpzKRpd78kc82j/xqXasvt83lmwIeCVMXyIQQ8lph4bXTLhw5R8jHQ57BS7fbaHaQX2OkcRZRcNH00MiO3I0KVeQzU2VFEkVSJ0m2u99Zd7XW0DQTjciBuux3ICODwHstBuuEmCQA0WtDLJ9mMY8uLf2FBdrhaVxVYq3RpaJMBybsQ1bluTP5Z+4Z5GZgRBh8B/2KKy85yDMvbWAM3PCmG7ni8muJoVWosxxw0SRhRXKii6C5DylEvviVu2kmQ8qyxBQFMVnWT53iwosvYSEEThw7zriZUI/GPHT3F1heXmb3gbOBH/7rf4C+C23u/GoZx5/r/Wg04ZZ+mmQFugUXmTq8sMVoxmCmg66u5o2pG+HJ9fNab1f+aydf22VGSgevB0QyRhRwu0yB7ucxyC7768oPvQNrSBGG422sc/g2UriCJsTuUMvrvK4ocXVJOxrhfeiupy7djTYvOP882vGYqi7BWopkZ5agjKLsxDkFk2C63yf3PPLh+jM//kH27d/FZ29/hEOvvMzSwiK7du2mLIWaXKjTHE4THqsbgUYPBUuiVdBUUVQk79nY3uSc887nmps/xhf/8g9Y3bOH4c6QzTMnGA+3OVGWLB7e+y18ir67bO78anlDTkZThsxsO8W1axqcs3P5Fl0qrxj52XI3dR0uy+vILknqKPpHYxSamut4Af2YGX+eMgFoD2LmjeRgMF0zMKHKuikwaSYs9AaEVpRvYxs1rReVXwsi/OmcgHdi0PvMhUYuMQxXXHop48mYleXdbJxek3Ik5NmHjjRfBzaIiviTu8yVS4yBSy85SGEcd95zJ5ddcgVPP/UEl11yKf3BAFcUJGO7aF8UBa5wmLKkME5Yf62hsNLw9EbWflf37uMTP/FL/NmnP8OpV1/lyiuvoL+wxEsvvsLOmWMkDDsnT/71PjjfxTZ3frXM955Mpp0Wx+8m3iZH+ryoI7P0rBBLrnW1U29M7pKbaR2s2PPcRs+9gMwbADPde1UIUpK6bmc+6rVzj52clTDtpMvfyzVSEyiKCswYjCWFIHJiJALgXEnV6xFDxLeemNFMuvIbNZ0xGJ59/nkmkwnjnR2cKxAyTt+VKfILRM1u5Bc1RkcZYXqfKQhzjysCe/fsIvmGuqxJGF45cpTFhQFVVdEvK6wtMYWjqkqqugZlPEa1BpKB4WhMWZV87w/+FE+98ARnXnmCa97yNt5w/sXsnHyRWz9wK/c9cD+njh6mtPOaP9vc+dW6RLfD3WtdrodAFticduQRnLuZTgqmrzfS2bd22g/oenRSR+TRoaD0LELUpyWGgbKqoWO8cbKLX1ZgHc4VWGMoTcv62mmiatHNptsGcEXB4uISq7t3szPa1mtr/mEdNgXKqqLXGzDc3JB13uQxGaCbyxhjsNbx6NcfwTcNvf4Cg96CLihFhUIoR8HrKhBZGc5lQUYAxpQY7mxTLhe4quKV117jshuu5yd+8Cf5e7/yc7RbW+zZs5dBf0CIkfFohC1LirKgV/aoioLdq7vZtbqHXau7Sb7l6uvewvMvPM0rh15k9/5z2P2GS0llj822ZP3pr3PJObv53vdey7ET42/XI/Udb3PnzxbVcRWPm+fcGXjTQW+RYBw0hTU6ajPk5p+BFAR4GwKRIDV7lOaVw6gUlkhuOVeI4q8rKXCqB1Djekv06kWwCR9ksy/5Cc1khG/HRD8iFJayXmKctiB5XLJIlq21cGX5gY//MIOy4I//5I+FWCRnGQYK4+j1+pTW0jSNMufkD2Q6pbDAwYPncPi1V9lsGiaTEStLK/hosMoeHLX3kMsRk+G8XV4wHXummDhzZo2FwRJ/4/t/lIWV3Vx88SWs7t7Dr/+zf8W//F9+nZeffY59e/YRkS1FYRyO+DYw6Pe45qpLeOuNN9K0lttvu52qqHjhlUNUBnxqefaxe+iVNUXdY7Cwm2dffJYz669w8kTz7XumvsNt7vydaVMqh3Eg4/CFGZeu+5wPgWhUfts5CltQuoqyKDFFRVFUhKKiwOAoCC7hcJJ6dwQcERc8TQxCQYUntiMmw03i+jG2UgCl48qMPylzAqbE2IoiMN4rLRakYDoqrzbAE489yp69qwyWlsTx1YlIhrKsWFpcYXu0w6RtVHIMjMkYh9zbMHz4wx/lM3/5F2xvbormvbFYomQSGu1N1/CUIyPThHUIRGO6NehTJzc5few5nn/pBBdd82aOLy0zHk94wzkH+fV/8pv84Sf/PV/+7F8wKGvpLTj5vb1vuebaG/jhT/wQC7tXuP+BxxkOh3ztzts554KLOXr0COPRhHrQg+VdpKpPiMu4lf1stoETJ575tj5V38k2d341n5Ruwlj9x2HqgsIWws9f1BSuB1VJoam4MyU+l+ZB9wFDSxsDyUskbdqxjM+SF1KcFDrmn25CELOuXR5xxQ680/EDduWIIPgNiRgTuEJYe7tcG/JBZpLhsSceFf9LkXPPOV+2Cm0BCYp+n2Qt4+E2vp3ICnHXS5iWPb3FBd7y1ht45oXneO3VV9ne3sFUDjsJXTkTDV3JYREdA2sc04VfWfRJKeFT5MFHXuTx5w5x3r5dPPrUU9z6vg9x8OJLKPsLlIXj4x/5BBdccAlf+uyfc+LIYWJIBB/Yu38ft37gfZx7/nlsbG3z6BNP0DZjRjvbvPz8M4yGI3yMDLc3WD9+AlOWrOw9wOLCIrZy1IPFb+NT9Z1tc+dX23fuG4mFDNW8MtqkmDDRk8JEWHFDQxyNaYMnxVZm5jEKgabi9nN6m6cAeSafdFymrUEF8+QWXS43RDRT1Gi6RV55jZVX2jyFsAYCwtPXLdloom4szij7cNsAhrKq5IrWKKmHY7k3YGe8jZ+MiTEq447SiRnpW5Dg5ne+m9V9+7nuhps5fvIMC/0BH7z1+/itf/0bNI18Fi7XEsZinUwTSFZLJg+KGjRADIFHHv8mKSYmbWR7fZ3Pf/7TvO1t7+CCy6/EFRUxJc45cDY/9CM/xT333M6TX3+EZmfIFVdfw5vf9GYwhu2dMfc/eC8pBOqqYtK0ciDquRoB2ob1U8cw5lzs2NBM5oo92ebOr7az8YrQYROV+BIF0kCrETF2O/6aHus+gI103fykkTw7raTgs/WvHAdTOWyrzf+IsbmTb4kZNajcf1OosTbPUlRSTavZiVUcAdIh1/q4rg0hJnbt2kNo5cAq+z3KsoaiZLK+hg+t3pdYPnKCLvPceMNN9AYLXH3dDfSqAT0Lw9E2vf4Co9EITMSZghhlkmCV+qvbX9B8QsqqpBRkiWQDp0cjjC3Y2dnmzrvuYGN7i8uvvBpX1kxSxMfEzTe/l/MvvJznnnmCm256G8sru/E+8fxzL7B+6pSSj8Lq6ipHjhxDOA5tB5AKbcPO1hmWlvZQLa58y5+l7xabO79aO9yUsZ3JK7Uyuxdyjjiz6ANglaVG1liMyWg9LXytIdk83haPNhrZIWGTVcJM1JcTzjhS3twzFmtFv14ShkRZ1RTO0bYTZapJlJXAbJ1z9PsDJuMhPiYW+gNSCozGDVVVYU1isLiX9Y3TOOfo1X2qomRjZ4O2nczK/3W/H6CHmeHZp5/i5nfdytrJo0w2TzOMLYVzwgMQk/QRFOln7VSJWJh70KgPcsQJLiDGiLOeZCJVXeB3WtpJy/1338XG2hneeP1bcVVNSBEfArt2LXHzu95LsbKHcRNpx0P+9NN/QV2WBO9pYmJ5YZmj9jiEjDeYTmIm21vEScNg1+q3+En67rG582cz2ujSr7vJu9EU1k5fByrOaRxePcep2KU883IgoAdIpvK0Rmph5wpCiPQLJ/UysLi4ACGyubkFzjCoa8qqL4w7JglvnnEsDHq03uPbSNWrpPE3HjEaj3HG0rYjNr2nrmti9AyHLUVVUbYtNhmqusYWNW2zQ7uzg4+BTi9QQUImZUQiWAf33XMnZ519DqFtKHQzMDMZx+gpbanCI0LvFRRCLIfezFJPkkYnQXoeW8Mxn/iB9/HNl17l0UeeYWdnyGQ04t57TjOcjLn+LTdRFRWtkpo24zEuBY6c2OClF57mmSceo1fJwk5MsHbmFHVZMo6x28DMHAgpRllmWj/17XqivuNt7vxqQmOtI+nUkW1LVDSCqDOOLu0HwcNXdSHpbAiUdakpLRTOYQ2COVct+sJKFuCsY9w0FK4AIk3jiW2gKAuqXkmKBuMc4/EOYCnLQhSCTGC408rsnMTOzjaVk8jXek+hwJcQAsPxkMqVgh8w0mcoqoreYInWj2mGQ0LrcQYaVQkSnP40/hsSZVGzvbXFC88+w4UXXyTKOAlQUBBAUTpMlPdNSn0u2Yud2VnIgKdcjyduvulKrnnjxUSfuO22ewghYBH9va8/9ACx9bzpLW+lKOWQ83i2Ntb42u2f5467vkpdVHotwRIMx0MGgwGTpkWEzbTMMIrfSBHv5zV/trnzZzOQCSsxhmAMDovTh8cawFlMpvJSGqyVXasU/ZrhxibWGSaTCcF7rJOIH3ygKEqB2vpICo2OxQzJaWecgA+RUTPGRJHbylMyHxomzQjnKqyFyWSC1VQ/AaOmlUiMNgEVnGMV/ScjOcEVLC3swpDY3tkWws4UsLbQhSMJ9bnDL5+JwTqHtZaHHrqfvfv3sbi0RIyeU2tncDOy48RWabNFU8BahzGWFDxZ6SdvLlprWBgU3HrLm4jAgbPPhq5MkBFhCIGnn3ycemHAlZddiSucMCfFxMuvvcbJ1w5TlT2RKosRYyytn+BixJUFvgldEzS/u9E+xNzE5s6vZnVhxBhZa3VIGRB0BOcTWFXgya8Prcen0wzGfcqqIEVZ921CIASPKyomkxFjZfRIUcAwxoo6jo9eYbWtzP+Rh9MZQ6stay8IGoIfCVw3RESHJ3WxNKWILQp8q7TgQBsaxqoEvG//QXGItmFrZ4120ogSL7pjMKvCmyf1JqkarsCPbYS777mT993yfhKBd77z3YzbyH133C70YCq0GaPg7JNOG2QLMRc+4FxBTImlQYlJDW0zprfkWN69i40za4KtSJFeb8DZZx9g795VrIt4L7c23NrhgYfuEwqyKO8XvJfDuKrAJeq6Flq2mLkSTcfSxNz3O5s7v5pXbrukiD5jlGefPJoTVF/SutWqTHTrW0bDbWxRUDqHs06vmAit12dNHKCDCkfJJpq2VWyBxCgh6JBXt20rqboruvRVXywAoSAPdkiy/0frp3LgZBy9OPJgsEI7GdMOh4xGIuLhXIG1FleU2lSM2t+Qn7NYrDP4KGQaAcP6mQ3uf/ABfvwnmLtvMgAAIABJREFUf5prr72eex55DIdltDOUoygmXOGkeak7EsbqGnOSMWChJKFFXVHXC7Q+sbR4gMuveSP3f+UOsJbdK7u54OJLuPld7+bD3/MBlheX+NrdX+PJJ5/jrru/JuVTTHjf4oNqHZAY1D2wljAZUlU17XhMljy3RqYOuZUzt7nzdxZ8S34yrJFIP6vl3qWLegBEHQcaY4ghYVpPkwdbGfUmf8IYgzN51CVNtUB2dqMpvkhOozWsSG0j0S0fQaGD7+gVOtTA61Lc15GEGIgEJs2Y4GW3v6wcRVHRHyxIxI5hur2Y12atlbIgGkIKFAaMcbz68st89gtf4PwLL+PuL3+RtbUzBBMpjKWuB1hXkEKkjZ6k49HugzNJM6KCSZvwIbK13fDCNx7nkgsv5dmnnmRlaYXLrriK97zvVt52w5vZtWsF3wbecPAi/s2//V0mox3ZQIxBsiDvVaEnMG4szoArDYuDRdabFhNCRh/RLUrNDZg7f2exm7vR0fF1kZaZgKGHQrdRm/K3p3Ra3faaJruZ335mXXA6UdBmIoQZIpG8t55khwByq6wTsDDaxDPKoDMN9DNYP4NGW0dVFPiioqz7FFXJ0vIu2knL1vYJYtcEk4ZfTtkdov4bPcTS4kzCFQWPP3wf//q3POPNTcqqol8OcFZovMZeiEJbL4CbGAMdBVpINHFC61uOHjtKYVo21zb40//rNj72sY9z7Y1v49ILr+BtN93IFRddSL9fMRqPeerpb/Cb//I3GG1tYq0jhEhoPW2QsinmA7VtSNbiCkevrhksL7CzsYUymGvjcx76s82d/69Y15zO/1FHVZcUcK0xMzEX6P6s/jb1zi6Nfh3Ix+T+oi7C5jI7/3n67tMqXJtmmf86VwEQMyeH7iXMMAsm2ezLaMKq7ssK7/ISlYmsba2zs7NFUF1AeZ+Mw8vrygkbk0wsCtH4c87x3OOP0hssUNUDQmjZ2dliMhrhvVf4Ml0WEjsAlBKJRLjg3FWOHT3Jn/z5XYyGET9puOK8S3jnu97PRecdoCgMGxvbfP4rd/CHv/97hNEIm6x27L3y+oeuB5Opw6y1FM4SmzGlMdS9Hk3T5A8HZ+eRP9vc+dU6h4FpVM+ul2R0Zcjsel1SIKm8ovi613frfx2Gp3uHnAtInZ26aJRBMLnmTzOXyqo35nUnDDo6zyQadN30fIMG6NU9meWDjPp6fYajHbZG2+xsbxHbdvoD6IjOoCSl0p/wMZGcknsUpTDsOEdZ1LR+zNraKcbjUQdvNin/NvpZdTyGen1XcM355/Jv//B2tjYbXFly8sQRzltcxlkYNxMOv/Aqf/gfP8mjD9yDwyrgKgghqZfJQuYbTMbgYtTJA50KUWmhXF5hZzRkNBpSWuEqnJvY/JPoLM+4Mz4+99Kn3pS61ylzrUa06YOtV8qhPeXvT/8s2f6U9aZLQrv52rQiyA6cD4RZNuF8+EiPQKm/jThZNKk7H6qe1OFVUVPUNTtbW+xsnWE0HtM2k64HIVglo/N5l/eZZdoRLT54CsCTKCgBKQ3W1k4yGQ07CjRLvg/lLjLTLnsyFmsSRVXyqdseInpU2jOxMRwynrQ89PB9HHr5EA/eeyftcCS8fT4QUpAGX9sK1VgMXbqfgZXGGHpVTdB+TEqJujDQGzAeDmX60DVk5zZ3/mw5Lc9Omqm1ugj7egBM9k6TPTUrUuaufn7wZ53cdD+lTb6pwyd525nDBmUNlp+wufegN6RBWTOSrNUjTQiLIbcYlpZWKW1BNI71UyfY2l6nmUyEsis3Ma2D4DV7iErlDW0rtbJMDwTIlFnvDVa66cFjrPQVwMhqsmZBchCIoKixCaMIwLIoBOJcGBofsBj27Vng4Ucf4PSJY0QfFQ1pSJ2QqCgR+9gQYoIkOgbJyJ4E1lLXJV7Hfs45ookQPL2yYHllmY21deYQn6nNnV8tj9u6qNs12oym5hJeOsm9mSwh/2smeDNtLaXu2l1FMBPVc6/evO7v8kEzhQaj/56SgCZxeoXQdn0Gpv0JjPDzxRjZ3l5nY/00k2Y07Tfm31Bn+t2kXycQRWHw3ijeQSXFoqAMjHUMlFWoKCpB88VIsNrwzBOHXPcYizPSLyjKAofQipuUWF6qufSis3j6Gy9DEtagFCMhBHxopLnnPdG3MglJUQA/uWBKibqqMMbiCi3TnKXvKj0gI8tLC5JhbGz/f39Y/jOxufOr2cw80zXSspOJI9nOM4WIsyu/84Ew6/iJLiPoWngzjBfdWWAkguYILLBdvURGFXb9AqTEkIG19gHsVEPATqcS036/ZiE+MBxuEYPHugJnhCk4xkiIHiJYZ0leonZUUU5nLUESf/KKM0byDO89iSjIu2iJQeWxIx1xadKVZoMyHFlD2asJPtBGicEH9i3zthsvI6TA/pUltk5vkXygDR7vWwUPBR3n6ejQJG28ymdY1zXWWow2I00SuS7rBO6MEVTh7l275t3+GZs7fzZNubsUXWG+8rWm3Zpmpy46zzbmpuO7vFCCMZqu5/HBtAcwE+S7+C4/rSm73tO0FQg5lzcIcn1aX9Nd1zD9GXlLQxNaDImq6mGtfC/4Fu8NKQURHp1REEopMBo2lGVBWZbQelI0eggIytHYkrZtSAlCiDrSm9J1SdpviXkUicUVBdYI3bdzlisvPchH3v9mXj52lNAG9q4u8fTTQn7iozi9AHhipxJs9MMw6tD9QZ/COqxzpJgorMNYoxmJ8AnEFCmLCoys/c5NbO78aibHczMNtjm57pyqY90F8hJQ5uQ3+bBIM2k4dIo9r8v3zbRJlRLJmtkBgX4xM040r79GLhFyk6tbNpq5Tr6GJdLGgLUOayVl9sETfJJNPOSWQmyn3UVVCWrbVleHS6KPqgg+hcq6spSDLATNbARjP51w5Dgrp1npCsBSlIabrruED7znOpJJ9AYDYkwsLQ0YTYbCq5Dy1mDOw1QUVH+1oihZWFigrCoKhRPnnkq/qjGuwPtAXTqKwmVycfrzbn9n809Czdip5GR2umnpPxNLZxywE+E0M6/LkbgLx1MH7tJ2Zrx39uqaKnfZgrFdHdLhBZAIKGo7UjLka+ZMImckIqElHH8gzTMfAqFt8SF0fIABHVUmEGKOUpqASNRsm5ayKvAEifBRUu4OGaiowKnjJ5K1KFkhKUFVlNIYLC3vfssFvOPGK/GhEXix9wQ7ZtBfktm9DyqVFmc/cil3rDj34uIiVVkRogCP2uCxtmRQFLiiJFpwCYqyxBWOwjo5KNy8259t7vxq4rAzTpT/ohuhSfIq9FT6M2a6LZYx7DMX634+j+o6NI6GqKyqY9B1YpI4bM4gJL7OQIXzkoqkvPl9pveeOi57ktFtP7k/aw1BiUO1/BbCT2bGlvo7WltSVlI7d0MEDK4Stp6mbQUFqLsJRWGJcYZrcCZrClmSXG6JD91yLZdctJv1jQ3qpUUWqpJeXTFsG4pywJ49uzl29Lh+1hlxKL9lVVVUdcWgP8AWjqqqCCnRqypM21IVJVVREIyoE1vrKApLSIaqdLLiXJV/vQ/Od7HNnT/brCMhjbjpoJ1pLTBTz+cjwWQdvS4Nf90l6fJRfYdp97/zEknJURhqLkGs6a6Tm2fWOugibcLi8gtkHdYqGMda3QIM3XJLV9LM5DYpBu1R5PBqKErL8mBAMFBag88RPsnXlS9JOEKMhKDrdlYylKhpR0qIyi7CcByDJ3nH8qBkY1izqz/BETDGYTCsb66za3nA7pUljh05Jndp5ZMoioK6ruj3xKHLuqSwDldV9F2BcZblpWUCiboqGbeR1ZVdYA074wl7+gOK0uLKisrOH/ls809CzfyVqN+5XQer0+6ycuplR7KpkMhuVYF2ZkKAeX1KbLraPWvPyZTBWe3wadrslGTTGUOIoo5LDCJT5cRZrHVaFRhMIT0GZwUJ55zFKWvG+iTIoYA27IxR6WsQrrtMQGik9wAsLQwYLPSxVu7NB08MQoYRgjh8wrGxPaawjjYluT/tyIusWZb6NnIQGUuIkb1n7aGuLXWxgiksk9GQjTMbnDlxhslWInph7bHGUZcFdd2jrirKsqSsCqq6x/LCAqM2sLK0RFkWOFcxWOhjnDT+KgMLi8uUpWNta5vdi8sUpaMsyzmZx4zNnV/N2alzTkfm4qjOqGwViuvPNbbJdXXi/2HvzWIty877vt+3hj2d4Z57a2529dzFmRSboqZICC1SASM4cWLDQGDJiSQ7fnDgKAOQBIEeGEAIAuTByUPylCAyZMB2bEuREtmSw0jRZEqORcki1eLU7Km6uqY7nHEPa8rD2vd2gzEHIaC7E54PKFTVuWdYZ9+99l7r+/7f76+Mubirq/EOnPfmI4BTKaw2eV+c8mrBWoVGEWWk68ZATAmjTL5Xx4RWkm+BMZuCFEVxsc+3Nn/meMVApdwpaIxGKY0PnrPbD/LeWwnEjM/2cezki+fqO7lYPSQlXL1yhaYpCHmfQCAytAMacCmRgqMdXL4YjOXC80l7sapIXOQDzo9jIrI+XbJRgd124M79JWerLQ9Olqx2nhDvkULgxvUbNFXFYnFAWZbM5nOUMUzqhqJqaOoS5yPT0ddPGUVdVmx3LdNJM/IBNVVhuewGjGS3H63AhfjP+/V/W8Z+8o9RVnWeaOS73UUNX2fsVhontSTy3UVrQgijqWTKferGwAj9LKy9SLwV1hABK4Zk8goh5WUDlako6oqu7cgiOSG4gAsDVVnn2rixIx4rUNqSkEJeYYzZea2zKAYZDSz1+SSPvPDK6zmZF1OeyH404zzvYnwjW0le1yQuHS0o6pLoM/aqKAwh5Ox/oRWr3Y5ZEl6/f4Z3A973FzDSCyny2AUhnEt4cwnub/z938ztvoG8mgAgEUUw2vAT//aPMJ8fYJTOKDStQcl4cYXCFrkKMH6YkkTXe6w2zGcHaJVx4BAxxtAYi5Jc8ydFimq/5z+P/eQf48rREUnyCiCKotQql4uIlNqC1qSUMDojseoiI6RSSohW2LHWnJfukaooQWcbLGs0BCgKi5dcbsp2fiFTZ5uGbbElhIS1iuA9xhiMyRcUl2Im+fqAMnnJH0NCqWy0mQCtBRc8pbV5izG67yQ35Az/uVDmwkQDMoU4Y8mTyEgvEp565pm8UlGKkM5JvBDDQFk2hBjQCJ99/otZJDS+Lvc5qDeVRM+3ObnESFQMnrE0KWS1wnnSUYghsJgfcunyZc6WS6q6piht1vInsIW5WNHocaWixBBtRInJEmIUZaHJRkYx26GllFdE47/3kWM/+ce4ev0aShtKY7LKTb+RhbfajvtzjdKQQk68FWUxsuozwcfHhNUKHz1K9Fi7ntL1jlndXJTGJlWVHYJGxZpSioVecO7bnbPnCWtLtFa44FDajMt6wdgx6RdCZgWKIaVIoVVe/iuDUom+94SQARooQUIuI8qYX4D8t7rQCeTJfuXqVUKIuJHiY1QupVkjWFshoil0bgDSOpfWstjHnZOzOG8+iCRSCGiJiC7G7dR5lpM3LlIpokTz2t27XL56nappcslOBGs0LgVqW+E51wBAigFl8/fNjL+cOF1vO4wGUZpJ0zC4AVAYLXTDfs9/HvvJP8a1q1eR0f02AtZoLIouhpyU0xlIabTOTjg+UFZlztKH7LQbJWGNyeq5AMYYRAnzScZmpVFD72KEtkcpaMqaqmzQWjM4l30CUsSHRFWWxDBgyxlGCQGhNBZRQgieQpcoE3EeCqsxxqKU4JzHWoU1AymFjAKLiSj+AugZ5VwzqEkqjiDRLBmu6wYRxdD3oAQjhlLIYhoSogzL1YqQ4kjVGS8q40opMwHf2AcoPfYAqFHdhxoNTskJypTzGYjmxbuv8d3f+d3j9iFXW6LPGZc+uLyKQtF1LVVZjMQjIcZA7/LF1Vo1uhsL/dATfKCLkWlRos2+zn8e+8k/xuHRUcZfG4NWekzagRnG9lAiRgxIyijtpCnMyK8fJaxKZz2+VhNSEoJPaKvHG3qkHwaMMdiYuDybsRt6Clsi2mb+PXllEUKkVqCkgGQZvKeqm9y5phUpCd3Qo7XC2gJlPFaZsVEmU2wArMr0YCUqm19EP/bc52+Us3DhvNBAiKBHYH8aVyQxRc62S4yyTGdTSqPZ7FqcH4jO42PCux6jMhMw79H1SD3m/1myHI/3eW8CFw06EFLkzu3bo2loLgOuux02Kno/MJnWyNjvXxSWITh86wl+YDKd5npGiqSYGQtd76mKvA0KIdCHQKX3p/x57I/EGFVTUeSeEYqiHO+gA/OiRCtDkDRqxxXGFJxrAPqhp9AGtEGRKEwGbu7cQFMaisLgYyKGkKEWKjfRYDSzaoEWwUfoe09VlxADXhLGGEKCylqm4wk7ODeahESqqkaJYLTOd1IxJDdgjKVtO6rKEuLIAEyjb5/PZbo4ehJkBFjm2yMaCKhC45zHWEuICasLjhYlfe9QQD84Cmtorl7l6Wef4nOf/aOxtJeBp8REOPchlIwQV0nAaMLofBTIxqJvCPck9xfEyMn9h3z+S19ksTjgYD6nsSXBey5NFvTBsdmOpERJOB8ATSAxDBkdpkhgFRI8pigYBodWGkPCKk3b9W/RGfb2i/3kH6OqG5JP+BiZNA1IxBqd4Q8J7GhJbW1xYXMtkjvJyqJgO3hUjDlxZi2N1kyrCS4MDF1LYQuUCFYbXHTEEEdJsSJET9NMKGyBixHtRgT22L5jjMnimqLAe4fSCu+GnPhLAWNMBldUDW3fU5aW0lpWfktZlqQALrjMtNejeWYMFzV7GfMTSYTFpSMOZjOW2x1lWULK40yJnHGPWQtw/+SU7/7wd/H8Hz0PyY5y5/P6/nh/Hy92KI2QcoVBAMal97nyL1+KgMBu1/Jrv/U7WGOYHxxwMJ/x7JNPcPlwgRg4PV3RVBV9PzCpCoqmRquS3g15ZSWJylrWu5ai73E+UFcVCmG1Xl10Qe5jP/kvYlpPxn1qhnkao9GmwoeAUblgFWLAViWEhA+5hh8RogvMyjqbYBbZBKM00PZbEEVhNC56uq6nmmiCaJQ2GG0hgQ8dSoMdP99YixVQhSHFwHbXIUZTaktS2QTEuQ7IunZrCmJwOD9QFJrKWkKMfPnFF4k+J/IKW1AVFSFBP3S0uw3hXPNPzlloJdx8x00CEWssiDA4j0UoS8NyucQ7z/HpGZB4+c7tvEKRNzQDGdqRiCGNovy8iklhyJWQlLsGz8k/+SkZfhpjtvJSKdK7npOzE1bLM47PzljM5lw6nHHj2lW2XYvEQAiK5ekZgxto6hrvIra03L1zF1MaHKDF0MaA6wM+Bazdn/LnsT8SYxij8SEnlvIddqCuavq+YxgGlM621t6FnP1PucllVtVsu45ChG0/EIGiMGy7gaq09IOn7wdmswlNUZJCRJMY+gGdDGFMHKYQMdawWW1pqgJlbO6NJ1FXdV4lpERIICFRVxVlYQhRIEViSGitaKoa73qsLbn11FP82q/+KoTctmuUwTnHZru+IPbKmABUSiFa8X3f+RF2uw6jFF3rR7Wd4mS5Zrlac/LwmNVuxdWjI0ptKIoqk4GSH3v2c87i3CZsbHbMF8nzRODY8ZcuWH9hlEhnHULX91AYiphwEmm7Fi2Rew/usV0tqesaay11VdJ3PUnAGoVPgo2JdugpypLO9aSQaKqaPga8y1SffeTYH4kxbFEQew/JZYZ/iKzXG4pCURUFnXesznY0VUU5t5io8kQaemZNxTD09EOPMoL0ibZtmc8mrM9O8QFW6zXTZobzgdJaqrLJij4iVmUKzXq9JQaHkpK27Wmakuih6waUNfgw0BQFmBIl8QL44QdHUZXs2g7jMjpbx54r165ysJhzdnxCUirnFoYORUJpOxprnNuJJw6PFsznE2Lv2RHZ7VpsYei6jrsP7pG84LyjMhbnI8bmJOAmBtoQxuamsYpwrpgcDTYyvTedz/u8478wCjnvSMzw0L7boVPFUOQrR58SVvLYT09PWJ5lgGjrOg4mDTFlJyRrLH3XopRBjGYYPJOm4d7d+xRlmUVS+2z/Rewn/xguJFIKFMbS9QEEDiY1IQbW213m01fVKGHLKjolhqIp8S6CBOYHc6wxxAiTmdB1AyBYk7PNwQ8oNL3rSCF33xWFRQDnwygi0gx9S1VUnC1X2MIw4CmjohjbVXvvSER0zBr+IJCcpy7LDK8IwuBbXNtz87HHODs5y8ANYp7siQv45RteAZp3v/OdaKXZuSFLZAtDCI4UYX16RlE1pJRwUZB+wPmBw6NLbDabUcIshNFY5I0eBIWxGqUFiRDVSPdVEZFcCTE6KyO1UdiioqgblFGIaIahw5OR20oVnJytmTQlapRKHx8voTAYIkpsFvQIxK4nekc39KgYaIeOfrejLvcKv/PYT/4xXO+I0dO2fT6xjHDWthzNZ9QhIMbSRc+sntEPXfa917BtHarUSMx3ra53pBTQ2tL2Pa1zTCczQu/wIWEs9K3HFkVWxCWVrcKE7JITHG0/4D2sdmsWakFd1mhjiN0WVRZ0Q8+8rhiiJyXB2pp2u0TXEyaTihRbtCohBJrJ5CKrH0IYl+LZEQjIIh/ynfo7n/sw3mUtQNvvUKagKjV95+i8Z2It7a5j5z3ee9q+QxfVaCyaRUPnHAEj+hwAxke+53tzovIcUSAaLYlkCwqlGVxPiOAirNZLNv2A9pHWDVQ6I8e6rmc+KWlDoh96hIGYBG00vu0olMZoGfWLEaUSymgG79ARRGJGG+y1/Rexn/xj5Bp9QVMqEIuPPbu+w/lITFAbQ102RMlL5r73hBQQowgBhrbnysEcIeA9dMOATlCXNSm53HxSV3gfsKagKi2r1QaUpS4rjICPkW4XmC2OWK3WPPrIO1it1mgBIwJVjXOeeTPBDR0RuH98TGkLKq0x1lHYGdQTNtsNSimuX76MEkFlf3F8zMIc5Nw0VHOOHy/LOusNRv6+SjFjuqsCPTbouBiyBDoJqqoZNt3YaJSViJn9p8fXK6ICj8Gn/Nmp7wnikZQIaiD1HpGEGzKkM4xiAE9CScShUWPuI4SBpIS+c0ymEzRZQqDRmNF7kBQvhFqlKXBD3sLVTY2QiHGf7T+P/eQfQ5RmfjDHDz3OedrOZ+dbHyhLS0oRo2DXdtS2RCT7yBuEdbfl8nTOph+QFBkGhwiYsuL+vQdMmwmVtTgXOVzM2XQ9bbtlMpmN2edEUVo2pys0wnqzwWrDar0mxYjRFq0U3a6jDwN1ndV83nseuXKV4AZczG3FD8/O8D6SvEcrw+M3H8t8O++zxh4IIWC0wsVz3n9uEiqMGeGcWRNfFpq6rlludhAjtigZYsyVChRGFO3qBKs0Aw5GxZ9RBudd7pUQzZ27d4jeIckg5BbjSVWxand5YmuVE5CicClLp1PwFCpftHSlMkgkBHbrFdYo6jSlLGzWOWiNIjsCR7I6kphQkqjrikmZiT8perQt39oT7W0U+8k/htWah6enhMFxtJijRZF0ou229ClRNROMsWjxuBBo6oq6qenaHZdmE1IKTJuGbZfzA0oMVVnwyPVrPLx/wmazoQ/HiDyKj55p3RAILJdrDqZT2u2OoiwzgrrvidFRmryH7QaX1XxlSegDVWlz1nwYMLbAmoLgHdZYvItogYBQNxUq5XZjNfT4ODL1lOBDHGGaAdEGjTD4julkxjZ11GWDtYqyrIirNbYuuffwAc1kiiSwIqzXmyz6KYrcwKTtWM4bG4VUduWtixKnQSdFVTdIAGsVYme5UiGSKTtaMbiA1YnkQCkYhp7T01NWyzPa3ZbgHTcefRQtmdprrWVS5s/VVtPu2tx7oRkJRVmSLSSCzdLgfeTYT/4xCluw3HXcfOQ62+2GsqmRMNClwLSekZSm61qUgrLMgp1N2zKtK1JURLIrbVVVWG3oB8dyeUZZFRR1wY3FdZLA6dkaxDOpJvg+IFbnszzC0Hc456nqmhiE3g9UtqIuNUPfo4qCfjlgdUdVV8zrmm3vMFrY7bZopSmMRSvNeujpux5lDI8/fpMXvvTCaJ6RJ66XCHE0+gyJaBK/8mu/wSc+9jHaYWDeNKxXS4wpsIVhUlTcv3ubg/kCXZYoa5kv5hhj6GPO/EcgOoeL4by7h5TIlQHRaGvpQwAXWW47CAHvB0DjQiCFnEsIweN8wChYr9fEkMuSVVXx2M0nuHzlKoeLw7xdKgyVsuxcR1mUGCIHi0ucnZ0iKRIAq0yWBe9SFi7tA9hP/otIorl6uGDVdRhT0FSKh/e2NGWZy0rec7iYk0JOHA1DoFCavh+YTiYksbiho6kqgs8n7+n6jIUcUNdTtn2Pd45qMkFroe270T3WEPCURYlve4xK7NodpEg79ISh53AypxsGeu9ppnOszXevtm+pbIkyhkeuXKVtO0LMevuqKOgHR2ML3vuu9/DiV15CTEAHQUuBJI93nhh87pLzkS99/gt87Ad+gMV0irGG+WxOWZT0fcv1S0c8/8ef5f6dV9C2oGmm2LoiZa/x3JjUd3kLErKxRxSN947bX/lilj3r3MUnWudlfsqrDURnnFjMKj/G54i1WKuZHEx59OZNLl+6zKSeUhQFZVVgTNZbWK2xqWC73WLKXPs3RUn0jsIIGoVRGl9G1n54K0+zt1XsJ/8YMXp22w22bogq0bcds0kuOfXOcXmxwHmHc55Ayn71WqFFs+s7mqqmqqds1ivu3b9PWZa84/r1vNcMicl0Su8CIgmrNV0KpAFiHGh7gyk1RSGjPj7z9C8dHFCKsO07tFJZIzCtWa7OmFSR7a6HBlzbZU86ge22ZT6dZfWcgrownC5PqcqawbmLbrnohmy8ETNuy5MoK0vT1LmzMOTleHAdu7YFa/jED/0r7HY7zh4+4Hh5xmq1JsYeRGi7Hf2uzQjwESGcRkdc5zXPPPEoprJolaXHQwxsV2sO5pOsT9CaxeKImBJLr2kwAAAgAElEQVR103Dt0mXW2y1aa6qqyCw/W9K2HVpB33dZeZng4eoMY7Ke4MrikDA4Hi53Y2kWnAvUpWWiFbbfn/LnsT8SYyzXa6ZVRTcMNFVBoUApS1GX9H3/RosqWZSy9Z7KFgzOcbZcIgeKk9UDQnBU1jKfTbGFpW1brCnwQy7nqQT37h+zOJgh4nNXHrDrWyQq5tMJMXhCErbtDl01aFGYQhF9YLNeEX32r5tOGvpuh7ElvcuCFmU8neuoy4qAEFIioqiaKd3QMXQdbbvJE390HEkqk36eePyJjLsSxeAHiqJkvdlkaXBpQSzNdMaVq9d4jzasd1vu/PwvsDk7w/d9btdVGlEmwz2URivFO27e5OMf/zhKa5bLNSkG5ocHDC5gUqS0BikqIPH63Yc0ZcGu7ZjPZvgoHC7mdG0LWlGlkuAjSM+uc6TkOZxOOFuu6WNitd4iY7/DbrdlNp/hnacbHMPQUzXVW3iWvb1in/0Yw1ibraDI++BK14gWoo/ZWFIJyUdOl+us09/sUCOA02hD27ZURqiM5cqVS4jObjchKbQWYhqIfqBte7IpRqSqGwqtM/dPDNYadt1A23UZ0uESD9dLVtstgwMkESVmwUq3w3nH0eKQYXCsN1sCidpmB10fsqZ+cJ77x6cMwRG9x7lhrPdnIpEoGZFZhnc9+06SCO3QM/Q9ZVnSTGe5E3GU52aCjsIUJU0zQzw47xFlKKzFFGVmBozgUiWKXddT2IqUAgfzGUob/OCzFRfQ+8iLL77EdtMxaypCipgi22xfuXJEjI7l6owYA851lMoznx1gjEa0ZlJWNE3N0XyKC56Hq01ercRE9IGu6/B+oChL1uu9V9957Cf/GJOyyLScGNm2LbthYNd2+KFlvVqyadd0rmcIDkMGZpw8POHLL3yFoijphoFrV69TVTVRKdbLNbttT4yeV+/fp90NrNuO1W7DZFLTdgOKgBDZDT2nqxX3T05IWtP2PV3Xc7xesdlsAdhtd5k1IIIWlduOo6cLHmUS88UBddVQlyWVKcdORDg+OeXBvQecHd9n1+3wMYCxWFOgbYExZb4I6IIvf+Ul8JmcK6JYnZ6yXq0JSbi/XBGiQyTDRIa+Z7vZEX3KKj1r0Dpz9pQ1KG2yQ49olidLXnj5lXyRHAbWux1tn0uRL730Kuv1hmHwNJVmu9vQtwOnJ6c8XK3o+p6z9ZbNdstqteTeg4e0znPn3l2c75lXNcfbNYQB53q2uy1t1zKbNCwO5uz6HlLkxvWrHB0d8I7r19/S8+ztFPtl/xhd79is11y+fETwAbQwuERqHdu2RStDHxxXLh3SbXcMfcfrmyXiPXa0hn79wTGdH+gGx3YYMH1LSImmKmkmNew63vHkE+zWayT29G3HerelKidsuiVJEtvNitYHVg8eYMqCK/M5LkaWp0u0VkymE5qp5mS9pDaas/V9oiSmc8Pp2QplLYezGQ8ePECLsOt2DN2a3WZFGP30QPCM7LxRf2+08PwXPs/3f+/3crI8ZbNa8q53vZMvPP88Tz32OLtth+s9PjzIhCIUr77yKn1yiB6ZgUqh0UgSAgE39IgIUeCX/9Gv8Kf+5Y9SVIZ3P/MMD0/PIHmefeqxcTMVWa9XHCzmGDSP3rxGYSxVUcCjN9jtHmVoO9799NMcXr48moFkVmLb5Q7HGBPD4Dg4mNO3LTEFBhdoJg0ueKKLWLXX9p/HfvKP0fc7EvDgdEllFKawGGPo2i5jqozGxMDrd+9xerrk8UcfYb6YUxjD6ckZi6MDXDfQbXYsh45Lh4ckU7I+eZC5epsdRmke3L1P27eEwdGUFYU2PFgeU5QlkNi2Ha7tiASqyQSfe2BpJg3dZsmDe3cwpsAlh6saprM5KiV2mw3bTQtE/JXLaAVlXXOJA6ppfYHaKsqaFBMuOKIPpJAdfFLwNNOGs+VD5tMpj964ynRa85EPfYDX7tzlmSdv8sort1EpMpmUCIrHH3uUf/LP/gBEEUJPHKElPnoKZTKvH4h+YLsO/G//6//CX/rLP8G7n30mj6fIIBIFdG5AK8NqsyIlYTabYa2GkPsEjpdnNEWJFqHte4qyIMVE8AOzyYTVZsOkmaJ0l6XagLUVtlSkkDFlgUi/z/ZfxH7yj+GHSNf1JImsXaAsLKvNhsVsxnazYrleUTc1/W7LM08+wdnyAZttz3a7pa4rqspy9+5dnnniCTofGboWQpf31AlOHj7ksccfxXnHux9/FkRww4AW4UYM7M6WmLrh2uVDHhwfs9zsKEvN1cPLlHXJbDol+ogxltV6hbGGyXRKYUp88CgRhmFgu2s5XBwQSfTdQFmV/Nbv/i4vf+kFZvMDtC3xQ48Z9/8xhtFSL/IjP/Jv8a994hPEJJRFlctwMfL48TGDG/J+XSkOD49Aab7y4svY0hBjTXQdEY/SgtGWyXSa990+l/2SZOuwv/U//xwffN8HODw6GPsMDH7oR3CIYjad0XU9bhiIURNDxKicV4kxstxtmRYVy7NTbFGSUkQrNW69utyboIQYIkHF7Pgbzl0XYL/TfSP2k/88JPDSV77IzccehRB4x5UrvPc9z+TJ5T2ByKWjSxRGY2zBbrujG3qMUlw6PMKPBpvGWHZtm++yRcHJ6TGv3jnmyuGEqm5omobP/P5neOqZdyKy4fjhMUeXLnF0c07dVFRVxfXr1zOVRmdL6955CqMJNmCNwVhDaYsMqHT+wrtPa81kWuNDRnSLCG3XceXoiOnBguniCl23wyQh4kgx4iV77KUk/MD3fC8pJYJ3bP1ACrmqMJ/PM6ZrcZjx3ynz/r/88suUpiRqh0ymhJTwIaCU4n3v/w5c3/MHv/8ZdFGRfACt6bct/93/8D/yH/57f5WD+TSbmxhLQvDeEWLClAXRB9q2QwAvmVkokuv1234gicK5gEhuM+77AS0KrTXd4LHGsBsGrKjMF0DQRjHEPcbrPPQnP/nJt3oMb4vYrs8++dxzz/HB97+fW7feyfXrNzg6usT8YIbRmsODRc54J7JcdLwbTWezrE6LEeeHrIEvDZv1mr7bcXh4xMGsZjadYowBJVy7eg0I1EXBwcGc+WxOVZXZkGJ04Dk35nBxQEkWE4nAdrsluwpl1HVGU2lcdMho7xVThmaEsZx34/o1Xnr1dR7efR03dIRhwPsh6+99GDX4igfHJ3zXh5+DlBhcZvaFURKcmfeCDwFrsmHJH3z2D/nKq7dJ3qPIRqDeO9ww8NJLL/L+Dz7Hq6++PMps3zAUXZ6c8dk//BzPPHuLuioztTgGnAucLs8wyhBCYHCZfhQT+BDwPk/ifuhwIRL8aCXu4+hdkPHpMQZICuc8/pz8OzisLdDK8uHvfO6/eOvOtLdP7Cf/GLdv3/5kUeYuMCVC3/eIQLvd0e62Y+Y6V/pjDDn7Lplz73wgDGNHm8klO+8cVVVfmHgMzrParqjLalziJipboAuDjP3vpAQ6m3IopUcsdTYS8T6MCOy88jgn3xhbABGNvjAKPffeMyb7CRwujrhx7RJP37rFZz7z+2iTEdwp5s9LISv9Xn75JT7y3HPMZlOqsiJJLoEO2x2mKEabv0hweUVw+/arfOXFr+D6nkQczTRkxINpXnrhSzz6rltMixLnAzGMJOEY2G42/O5nfo/eZTZhQuF9xDlH7z2u93RD7uLzLuDcwOB8lhD7iB9BoTEKIQ7EkKGhznlCiAyuxw/DeFFQKG3YbNbYquIj+8kP7Jf9F6G0Jo2JPZHcfhtCvmNNJlOUyYdKEGLylGVNP3TUVqMkEZUiBE8/ikkEPbYD95lbFxKzZp6hm85hdG4FlsRoupk767TKrj9a5Sx5cAk/+NHFN5NwvB/QUtD3PcZogvNEEUpbEKLPUMyYUFZnPDbCq/fu88IrdzBFge/73OtfVWzbNhuQ2oLrN67y6M2bDM5higKtFIMbEK1zZaLdjquQLvcRFBYril4S0WX23pNPPM3x6oy7d16HBC//8RfRSvHnfuQv8vd+9mcQo9FRiD5wdvyQv/d3/w6fff+H+NB738Otd94ihSx8isETQ2Dw/k2+CZoQA9FHlLFoBduhhegprRlXPNn9x+iCFLInYTdEQsi6/n7YJ/zOY5/9GOPcUFKrrDNfbzYgQlFk9RxAxsLnJhWlyauBlIm8ZVlQ1lmt14/ZaGNG6u0opknBMwwOFzLXDwEfssR2vdtiVK6VK52Td35U8jk3XPj+kWQ0AAmENBpukrPsznv0aBQSgmfX9XjvaNsdv/7pT3PriceQlJNgRlvc4OjabV7laM07Hn8SYw3T6ZQUEv3g8M7hg2fwDtcHonPUhQUi80lDDB58wHsPCW6/+jIqZglzFDJHP0V++Rd+gR/98b/MtWvXSSHTdkxRU1Y1r73yIv/oU5/il375V9hsdrS7HZvtjvV6y7Ztcc7lfn83sN1sxyx/fsxolfUF40VTjft+lNA0U+bzRYaRxkwhtnZP8jmP/Z1/DGMMMaRM6LWWCvKyWjQH83m2odK5B0BkvJ/avBcWk0UvMiKqnQu4GC5cdVKKeVKmxMnJCaYsScExnVuqegIxMC3sqP6LIPkiFGNuclGFJqTI0PU0Tc3J6RlVYZlMZ2yXK5r5hBCFFAMoIXlo25ayKnHeobVhtVxz9+QYXZbUMbHd7VhvViNgU+PcwGI2YRh6EgXRBVzwdLuWzXZLUWQisEKz2/UMw8DVo0t813Mf5PkvfJHtdstqtaF3HcNJdtZJAjEEhMR2s+Yf/OIvsNttKMoSZTS6KNFaEDHosuHVO/f5h7/6a/zg938flS0QETR5G6NMXpmVVU0MkaooUKYY3ZIySbks8u8wpqygNDqLjcqq5GCeRpuv8NaeaG+j2E/+MVJ8gze/3q6ZNhMS0G1bykl1sR8XMo9PaY2LARcdldRUVcW27Sm14ujokBADVVHlk18YjT4i88MDoguYSQNASB5SwveehPDKKy/x2M3HiWi894jSOaMeE44BpQzTZgIK7j885vKlI7RVhCEz+OkzPXc6nQCZZNtYS98P7LYtsc8ril27Ge+SJjfhCDw8Ps1o7genVHVB2/fstllA46PHDwkfunFvr5CkePrZZ3nq2Vu4ruPBgwe8duc1br/2GsvVGhkSQTQ+BSRGVssz9Hj3Pbp8gyCCI+DaHh8jppywXK747Of+mI986AOoEWJSWENdlog2RAIqJYqyIuS2yHyxlIxRr+qs3U+5fpk5gWYk/Ch1bh60D/aT/yKMNnSuwxSWqso931opxCisNgwpQzys0hRFCURSMrTdDmsLQgzMpg3eeXo35GRgyEScmAJ3Xn+NxeIQqw3KqlwS3G2pTZUbfnRWzT399C2i9yid0DoDOUPMPvdGa/p2iw8eay2Xj46y+6732UBEhBQiYTTkKAtN8oHddoMPiU//40/jfeD07IREojAloiQbdEbP83/0PJ//4gvZMMRohjFhZkXhYyR4RyASekdRWbQo+mE0DRFhsVgwaSZcv3aV1XLF3fv3uXvvAZtti7LZxssWZW7XlUShhdg7vGtzqc/tkAiv3n6JD3/HB9BKMFVJYTS6sIRhyCsGyf6IZiy7lmVFHEk+1haklKXDIpknoLTOSc6YASb7yLGf/GPk0nXi+MEJR1cW9N5DzBns3juWZ0vm8xmQIZBaZ/pP0CVtuxttuIssStHZQUfGcltInoP5HEmRlLIbT98HrLGkkPDeU9hyhE9mG6pCZw39drkiiaKqKno3UNiS6Wx2YTXdDQPTSXai9TFgJOvrY/RoU2GKguizV8B6dcp6uUS0xo6e9YUpsgOxqdmu13z2c3/MZD7DmHyBIpH5fUoobUnrWkrRbDZbtMqrBh8CKYacbNOGZr6gqBom8wWP37zJ57/0AnfuPUBiIPoBiZqz5XFm7oWIdz1+bCPWxqBndbZBK0qsUhlQojXNwUFOpooi+oGqqimLMhOMgqPvOrwbmDY1LoZcsUhhvDBC0pqwV/hdxH7yvymsscwPprTbjrIuETHIaDVdT+qxS00IweFGQxplBInZvy+kkPn/MUBKPHjwkPnBAiVglSZJou3abPU9mmRoMUQd8SmbhLZ97m0PIWKUpqhrtGi0hmI+Z7vpEAQfc1PQZruh0LlFSBLsuu3IyM819+ADxw8fElOk3W25/sh1zk5XWFGIzvzAGHKp8sPf/1H+6e9/ho989/dgFVRVRCWFKcucFwgDpbUU2pIoiTFDiIzJ4w3eZ8QYiWEYODwUYvBcuX6dz/7hZ3nx1dsM7UCKgSGEnG2WhBsciYTRFqOEx9/xKCgLJKzRVEXNtlszm0xGN2KNVjNCDBQ2uxpZY9FVYDI5YHCeYdcxSCYraZ0tzGMCtU/4XcR+8p/HeZlNx7Ecl9tNtWTeXWEyUCLbdBt8ykq26ANVVZFi5PT0lIODI8rC4rynLE0mdA2Roq4RNVpWK40SxTD02TteZXpuCpGqLJEkxCQYLZjaIIksIEKYTGp8GJf5JC4tFrne7fO4S2MJ5DvodrPBOccXvvRlQoL54oj3vO8D/M5v/Dad66kqTRyTZUHg5N597h8/wKRIjJphCEwaS4iessg2Y8aYcYuSGX3KZPttTcKlSGVtNt0MAaWFqizphp7Lh5f4SLvl3r173H39Lg8ePqTtOrp+IBQerRWL+QFPP/M073rv+zhaXKIqC5pJg9WGhUzzccskb4xS2ZhTFFVdj7DVnBupmhJjhEikLEu6rqUsSkAR0/6UP4/9kRgjq88U2li0EpYnG37n9z7Dxz76UbzLtfN+lPOWVUFt67xMVTpP4ATT+UHeQ5NdcefzRe7BN9nYM0k2ryAGuqEjRUEXBlQ2qqzqBmWEvh0y6mu7xdoSo9WFGadWmhe+8hLXb9ygqjIAw3XZNlwr4WS9pCiyMWVRWNqu459+7rOZD9BMuX37Ltpayvl8NN7MpUjveu7few2thH7wmErQpqDrO5KPNM0k50BEGLqW6cGCsrAZOKo1KiY2bctkNhvNOjS6MBRKMU+R4WCBiOLWrVskl0AnBteTXK5u2LKibApK02CtJiXBmHHPrhRI1lNkDYQmRk9ZVQTnKeuKzOpUaKXY7XY5tzBeaCcTjdFZNLXZ7uW957Gf/GOklNjtNpR1jYqKsqn5l77nu1FK8tJydNhNIohkN5oQIn3bYevygoRLOm+aze8pCDGGC/2AEo0PjtJWWdVHIoyOvW3fYUPei6eYKEYHHiSLdowuCGHg6Sefpht6zs6WLGYHRLKXYAiesqpz3T9lTPekrpk2DbUx+AQ+JK684yZJG2xMrAcHIcM4RNlRXZjHX5YWaxvKokArTVNXGKsJrqSZNBRVhS1K9Di+uq4xNktztTEE56hmM1KKiIa6bChKSwqgjMrGnClhbG6JtqUhhZypDy5Q1RVhcFmsJIKgL963LJqMRSrLfHxE0MDy4TH94JBpgw+JB/fvcv3adSaTis1mm4Vb+wD2k/8i3OByFl/ycrEyJVvviCnDKpRS6EIRY9YC6KhQ1rA5XnNjMadte7TJy3mUYDB0raOsNCZK1gkoIfhAUVVUZcXJ8oxJXSEq5wC0FkJIhOBQSuG8Z1JPgNyBn8jZbJ89scY6tqasqkytCTlhqI1GiNkGHHjsxjWCixSmZHb5Mk1Vsdq2vHr7Dt3pKV5pjIkQMu03eMdsPqMsSpqmwoimaSrKqs7lwZlCJeHo8BKD63n99Ttcu3qNqp5mfv45RdjIxcVQ6SlKj5n2JBe9B9aY7EYUAykEHtx7yKSuKOsJru8hCWHw9F2LD4FhSDxy4zIhBCQlBEPX77LycBhYb5YMbY/3HVprbIosT4/ZnEHdNKh9P/9F7Cf/GM4P1JMGoy3eO8RommbK4Bza2tGyOusBuq7H1jWD91y6fCUnq4zOz9WG6CJKshFHvmNlIUrbdtRlSUyRoe8wWgguEAUseqztO0iBFBR1WWUFncpwTu8GhpTLVQmw1uSyINmwI6vaJsSQhUp+cIhS3HrqSb7y4m0iju3ZKd5olm1Lv13j2zXeeUJwGJXLYaI10+mUg8WcSVVT1w1lWWSLsTg66iZwvscWlkeuXEGXBVVlSGQ7cIhoUSyXy7z/t4q6blDW0PUdEuHh/Xs004aT41NEgSlKwuAQFdht19TVuDoKAed8/oxS8/KLL6AgY9BsVlm2ww4iHB4dMXQ9s9mM23deo54vuLSY44KjsFUGfe4D2E/+i2imE7bbHZWNeS+csjuuSMR5hdKB4D2FthRlQdf3aGPYtTtmZo61eX+tlMaYLJ4JXT92k9nszJNyTbptO4JkwIQLgfsPHzKfTDk8XOQWVQd1me+GRlvuPnjItauX8l3XGFIIKCVstgPlvBxNKbLNtlJCUII2htl0RiLx1GOPc+ngn3GyXtMuT3jQtmzajt1qxa7rsuotRpzk3ELwgeA8q9NT9CKLhlIss7+fSoTB5TKnz7qDEAIHheXFF28zrS2L2YQgEIeBzuUtj6HA9WdUdcXgBzbrjiSCCxFRwuFshq0rNpsdpbXY2tAPA7XWJKW5euUqg/Psui3BBw4Wi7HnIldaZnGG0halFFulUcZw6dJljB1xZdZw5+4J169feqtPtbdN7Cf/GDHkpJlohYyTp6EmEkghb7zruqHddZSlJYT8+Hw+Z7vZMp3OODxc0O52iOQ7KFphxI5NORGl8/K+qsvR7y7inOeJmzcz7iplw8v5QZ60wTmUVly7cilTcUuV7+p1Teo7jHFZUjxCRFGKGPP2xfU9ZVVydnbKfLHgyccfYfv5L5F8TooRHEkSRhJRElFBoQ3P3Hqao6M5jzxynel0gqTEZDZlefKQduhRRjEpa4zVeB84mDaECLbQPHblMqIi1aTBpcSXX3udd956J01VZBTZ2YpmMkMNHatVh49CmYRd7zg4UDRVw6SZEoOjnk7ZbbZ45yjqEjcMeBcY3MCjj9/E6oIQIrtuy3xWoGyJlkTXOZQS1Oh4rIgsVxtIgdC37HbdW32qvW1iP/nH0NZgiyJ78hnNq6++xuJgxmQyRUoFpKzfl2wdnQjYwhBc4HOf+xzvf98HODw6JETQBJQ5T14xtuBC1BBTQhL4mLJEFsX9ew8orObk7JS6rrl69TKIYbfZIiSWm22u0V+5zHK9YzatcM7RNFO6th0NKHM77K7bMpvMMUqxXK0ojGbXtnzgO76DS5cu84XP/RFnmzVDu2TjO7zrmDQN167f4P3v+yDvePQGRpdcubRgOp/RbzqqsuDwiSfZrJZ5/DHS1BO2Q89iccjpek1d12yGJYuDQ2ICGwPvuvUsr7z2GjeuX8f7ntOHx2w2G44uHXHj6hGdj8ynE6y1zA+nrE7WDK7n8uUjCmt52LYcHmReAtowKUtCyurGwhgGF6iqEu8GjpcrrE4sDvOxC95TVTU+DITomM9mHB7MMcW+zn8ecq5n38c+9vHtFfvsxz728W0a+8m/j318m8Z+8u9jH9+msZ/8+9jHt2nsJz8gIi+JyE99g+f8jIh86mv9//9P8a38biKSRORHvxXv/abP+DER8d+i9/5rInJbRKKIfPJb8Rn/ouKbLvWJyHXgJeAMuJlSct+qQb0F8RFg9yd8zU/y//GLp4h8P/CbwJMppZfe4uG87UNEHgH+G+DfB34OWL+1I/p/F3+Sk/cngF8CjoE/860Zzr/YEJECIKX0IKW0/ZO8NqW0TCmdfmtGto+3aTxFnjO/mFJ6PaX0trb8PT+/v1Z8U5NfRBTw7wI/A/wN4K98E6/56LjE+2ER+bSItCLyeyLy3vHPb4nITkT+iYi8502vOxSRvykir4yv+YKI/MeS8brnz/kZEfmUiPwVEXlZRFYi8gsicuUbjOklEflpEfnvReQY+O03Pf5Tb3reoYj8HRHZisg9EflpeJPjE197G/CNxiQi/8G4bNyJyK+IyF8cj9OjX2fcVkT+KxF5TUQGEXleRP7CVz0nichfFZGfFZG1iLwqIv/J13nPJ8h3fYAXx9f/n1/1nG/0XX5IRH57/D29JiL/k4h8M/rZSyLy98fje0dE/qOvet+fFJE/EJGNiNwVkb8tIje+6jlPi8jfFZGT8Vj+oYj86a/xXSsR+TkR+aNvcJx/eDxHexG5P54nk/Fnn3zT8XplPF5PfI33+Qsi8rsishSRhyLySyJy6xsdFBH5uIj85vh9liLy6yLy9Piz50TkH47j2ojI/yUin/iq1/9zz++vGSmlb/gH+ARwn7xNuAEMwFPf4DUfJXtc/D7wg8B7gE8Dfwj8BvAx4N3AbwG/+6bXXQf+U+A54EngR4EN8ONves7PAEvgbwHvA74PeBn4G99gTC8BK+CTwC3gPW96/Kfe9LyfB748jvu9wN8cX/eprxrDV///644J+LOAJ28ZngV+DLgzHqdHv864/2vyiuvPj+P+z4EIfOxNz0nAPfJF+mny0jQBf+prvKcG/vXxOR8Zj/vRn+C7/CB5q/TXxu/yEeDXxt+tfJ3vkoCT8XW3xmPhgT/7puf8JPDx8ff/vcA/Bn79q86Re8CngO8fv++fAX54/PmPAX789yF50v4mcPh1xvWBcRx/nXxe/qvAK8DPjj+fjr+/BHxoHIP+Gu/148CfHsf1IeAXgS8Bxdf5/I8Dgbyt+CDwLuAvAe9603z6d8jz6Bbw0+R5eOsbnd9f8zO/ycn/88Bff9P//wHwX36Tk//feNNjf3587M+96bF/c3xs+nXe678F/vevmmgPgPJNj/1nwOvfxOT/P77G4z81/vuZcTw/9KafF8BrfOPJ/3XHRL4S/+z/3d75hVhVRWH89wlBGhIF2kOlRZqlGGMm/TE0CUlNsoeiwLAsQYksywpN8MEhM+wfhlKUJFnRQ1jKFCaZWWplWo066oAgCqKBD4FS1IOrh7Vvcz2ec/eZe6eRuPuDw9y7z1lrr73PXmev9e179mTqXkYN5wf6AX8Bj+fck68zTrUic81B4KUa/V7ORwMAAASASURBVHF7kLsqU16mLd8AyzJyg4K+lojzZ/vgI2BbDZlRQe7y8L0VOAFcVHD9I7gjXwl0hL66MDI21gI7M2XT8Ifs4MyYLnxQF+i+NMiNrXHNd0BbN/W2A4ti47voiIb9Idyaiof7FawBZkoqQxi2V30+Ef7uySkbGOrrI2lBCPtOSjoNzAEGZ/QeMLPqbVmOAZeVsGdn5HwlBdlRKTCzv4GfSuiO2TQc+CEj831E5xD84fNtpnwrHpVU49fM97J9kodYW8YA80IIejrcp/3h3NCI7mybt9PV75WU8cuQupzCo0PoGgOjgR1Wm6fpE+rZB9xnZrE3ekaQ38eqtq0MJLVI+lTS4WD/0Yz9eRgNbKqhc0AI5w9K+j3094gcnbHx/S/KOO9j4bpd0llpbyVsXBeRr14VsBpllQfRfGAh8AzwM86oPg3cndGb3YY17HkTRYzYa2Rn9zI21fsyRVZOOWV59de7IhFrSx/gZXzGzOJETlktVPM5g/DIci2wBDgJXIGH+NUEVqwfzwBteKg+HNhbwo4inaXvmaR+uBNvw0nySl90cLb93a1nDR5ZPQ8cBv4EPs7RWZq4rjkw5ETfLGAp0JI5PqAE8VcHxgEbzWy1mf1iZoeIzyQ9iY7w97ZKgZw1HdMDuvfjOWw1bonIHMLD/vGZ8nF02VovKg5ez/Y2u4ARZnYo54ix4Nk23wocCJ/HAH2BeWa23cw6OTd62Q2MrZBxRTCzObiDbJE0KmJTB+f28XjcIfefe3khrgcG4OH4FjM7gPMOsUllN3BXjfPjgFVmtsHM9gLH8dWHuhGbFSbhT5u3zWxf9QG8B0wsYjwbQCdwh6QJkq6VM+0393AdhQgPmw3AymDDcOBdoH8PqH8VeFD+Q5EhkmYAMypVF9jzB7ACaJV0v6Shkl7A89GlDdpzBJ8hp0gaKOnibsguBqZJej2EuddImiRptaS+Edmpkp4IbZkLPIATbeDEmAHzJV0t6d5QVzVW4WN3vaSx4bqpkiZnKzKzJ/GUdbOkm2rYtBy4UdJrkq4LTPqbwIdmdrSGXBZH8If13NAnd+KcVSx6aAUmS3pD0g2Shsl/rDQsnO8EpksaKakFJ2Mb2pMs5vyzcSY+r/FbcVJoViMG5KA16F6P52yX4IO/N/Eonj+3BVuO4aRRQzCzdXjYtgAPQ6cDlX8XXSsnXQS8gzPBHfgKyENmtrlBe37DU6wF+EyyvhuyW3DGfyROVu3BHfgUZ6d1eViCs9vt+MrFQjP7JOjdg68EzMZn3GeBeZm6j+Nk5Sk8RegAXqRgdjWz+cBbwFeSciOtUO89+Gzfjqcdn+N8U2mY2Un8/kwMdr0S2nAmIrcJmIJPdD/iufvDdPXlTNxfdwKfARspx0MVIr3Pf54haTHwlJml/aUSehVpJ59ehKQLcELzC5yYmQA8B6w8n3YlNCfSzN+LCEujbfiyTn+ctX0fWG5m/8mLKAkJRUjOn5DQpPhfv5WWkJBQP5LzJyQ0KZLzJyQ0KZLzJyQ0KZLzJyQ0KZLzJyQ0Kf4BaSjfZk2G6NEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "endpoint = \"https://computervision-gzf.cognitiveservices.azure.com/\"\n",
    "analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"E:\\pic.jpg\"\n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path, \"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"15f98ea70f74425382a7f6b768cd9915\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://computervision-gzf.cognitiveservices.azure.com/\" + \"vision/v2.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://tse1-mm.cn.bing.net/th/id/OIP.xNdAzadbe5eKue0hgl0CJAHaJy?pid=Api&rs=1\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"dd748cf10bf9404399e5416d9399e218\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取印刷体文本和手写文本 \n",
    "* 假如我们抓取了1000张网页，出了文本信息我们分析以外，还有每个页面的图片的信息，我们可以用提取图片文本的方式，将图片的信息也抓取下来\n",
    "* 我们进抓取了图片，想知道这些图片的内容是什么，也可以用提取文本的方式进行提取\n",
    "* ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"status\": \"running\",\n",
      "    \"createdDateTime\": \"2020-10-23T14:44:52Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-23T14:44:52Z\"\n",
      "}\n",
      "{\n",
      "    \"status\": \"succeeded\",\n",
      "    \"createdDateTime\": \"2020-10-23T14:44:52Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-23T14:44:53Z\",\n",
      "    \"analyzeResult\": {\n",
      "        \"version\": \"3.0.0\",\n",
      "        \"readResults\": [\n",
      "            {\n",
      "                \"page\": 1,\n",
      "                \"angle\": 0,\n",
      "                \"width\": 474,\n",
      "                \"height\": 355,\n",
      "                \"unit\": \"pixel\",\n",
      "                \"lines\": [\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            77,\n",
      "                            63,\n",
      "                            401,\n",
      "                            64,\n",
      "                            401,\n",
      "                            92,\n",
      "                            77,\n",
      "                            92\n",
      "                        ],\n",
      "                        \"text\": \"There are flowers growing upon the hill\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    77,\n",
      "                                    64,\n",
      "                                    119,\n",
      "                                    66,\n",
      "                                    120,\n",
      "                                    92,\n",
      "                                    78,\n",
      "                                    91\n",
      "                                ],\n",
      "                                \"text\": \"There\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    125,\n",
      "                                    66,\n",
      "                                    152,\n",
      "                                    67,\n",
      "                                    153,\n",
      "                                    93,\n",
      "                                    125,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"are\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    157,\n",
      "                                    67,\n",
      "                                    215,\n",
      "                                    68,\n",
      "                                    216,\n",
      "                                    93,\n",
      "                                    158,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"flowers\",\n",
      "                                \"confidence\": 0.984\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    221,\n",
      "                                    68,\n",
      "                                    283,\n",
      "                                    68,\n",
      "                                    283,\n",
      "                                    93,\n",
      "                                    222,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"growing\",\n",
      "                                \"confidence\": 0.944\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    288,\n",
      "                                    68,\n",
      "                                    326,\n",
      "                                    67,\n",
      "                                    327,\n",
      "                                    92,\n",
      "                                    289,\n",
      "                                    93\n",
      "                                ],\n",
      "                                \"text\": \"upon\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    332,\n",
      "                                    67,\n",
      "                                    361,\n",
      "                                    67,\n",
      "                                    361,\n",
      "                                    92,\n",
      "                                    332,\n",
      "                                    92\n",
      "                                ],\n",
      "                                \"text\": \"the\",\n",
      "                                \"confidence\": 0.83\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    366,\n",
      "                                    66,\n",
      "                                    401,\n",
      "                                    65,\n",
      "                                    401,\n",
      "                                    91,\n",
      "                                    367,\n",
      "                                    91\n",
      "                                ],\n",
      "                                \"text\": \"hill\",\n",
      "                                \"confidence\": 0.983\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            121,\n",
      "                            98,\n",
      "                            363,\n",
      "                            98,\n",
      "                            363,\n",
      "                            123,\n",
      "                            121,\n",
      "                            123\n",
      "                        ],\n",
      "                        \"text\": \"Like they always have before\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    121,\n",
      "                                    98,\n",
      "                                    152,\n",
      "                                    98,\n",
      "                                    152,\n",
      "                                    123,\n",
      "                                    121,\n",
      "                                    123\n",
      "                                ],\n",
      "                                \"text\": \"Like\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    157,\n",
      "                                    98,\n",
      "                                    194,\n",
      "                                    99,\n",
      "                                    194,\n",
      "                                    124,\n",
      "                                    157,\n",
      "                                    123\n",
      "                                ],\n",
      "                                \"text\": \"they\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    198,\n",
      "                                    99,\n",
      "                                    255,\n",
      "                                    99,\n",
      "                                    255,\n",
      "                                    124,\n",
      "                                    199,\n",
      "                                    124\n",
      "                                ],\n",
      "                                \"text\": \"always\",\n",
      "                                \"confidence\": 0.962\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    260,\n",
      "                                    99,\n",
      "                                    298,\n",
      "                                    99,\n",
      "                                    299,\n",
      "                                    124,\n",
      "                                    260,\n",
      "                                    124\n",
      "                                ],\n",
      "                                \"text\": \"have\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    303,\n",
      "                                    99,\n",
      "                                    362,\n",
      "                                    98,\n",
      "                                    363,\n",
      "                                    122,\n",
      "                                    303,\n",
      "                                    124\n",
      "                                ],\n",
      "                                \"text\": \"before\",\n",
      "                                \"confidence\": 0.952\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            65,\n",
      "                            124,\n",
      "                            418,\n",
      "                            126,\n",
      "                            418,\n",
      "                            158,\n",
      "                            65,\n",
      "                            157\n",
      "                        ],\n",
      "                        \"text\": \"Will you stay here with me, or go and killy\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    66,\n",
      "                                    125,\n",
      "                                    91,\n",
      "                                    127,\n",
      "                                    90,\n",
      "                                    157,\n",
      "                                    65,\n",
      "                                    157\n",
      "                                ],\n",
      "                                \"text\": \"Will\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    98,\n",
      "                                    127,\n",
      "                                    127,\n",
      "                                    129,\n",
      "                                    126,\n",
      "                                    158,\n",
      "                                    97,\n",
      "                                    157\n",
      "                                ],\n",
      "                                \"text\": \"you\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    133,\n",
      "                                    129,\n",
      "                                    169,\n",
      "                                    131,\n",
      "                                    168,\n",
      "                                    158,\n",
      "                                    132,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"stay\",\n",
      "                                \"confidence\": 0.97\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    175,\n",
      "                                    131,\n",
      "                                    209,\n",
      "                                    132,\n",
      "                                    208,\n",
      "                                    158,\n",
      "                                    174,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"here\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    215,\n",
      "                                    132,\n",
      "                                    250,\n",
      "                                    132,\n",
      "                                    250,\n",
      "                                    158,\n",
      "                                    214,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"with\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    257,\n",
      "                                    132,\n",
      "                                    284,\n",
      "                                    132,\n",
      "                                    283,\n",
      "                                    158,\n",
      "                                    256,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"me,\",\n",
      "                                \"confidence\": 0.941\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    290,\n",
      "                                    132,\n",
      "                                    309,\n",
      "                                    132,\n",
      "                                    309,\n",
      "                                    158,\n",
      "                                    290,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"or\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    315,\n",
      "                                    131,\n",
      "                                    334,\n",
      "                                    131,\n",
      "                                    334,\n",
      "                                    158,\n",
      "                                    315,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"go\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    341,\n",
      "                                    131,\n",
      "                                    372,\n",
      "                                    130,\n",
      "                                    372,\n",
      "                                    158,\n",
      "                                    340,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"and\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    378,\n",
      "                                    129,\n",
      "                                    418,\n",
      "                                    127,\n",
      "                                    418,\n",
      "                                    157,\n",
      "                                    378,\n",
      "                                    158\n",
      "                                ],\n",
      "                                \"text\": \"killy\",\n",
      "                                \"confidence\": 0.638\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            128,\n",
      "                            162,\n",
      "                            346,\n",
      "                            162,\n",
      "                            346,\n",
      "                            189,\n",
      "                            128,\n",
      "                            189\n",
      "                        ],\n",
      "                        \"text\": \"On a foreign lonely shore?\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    128,\n",
      "                                    163,\n",
      "                                    151,\n",
      "                                    163,\n",
      "                                    151,\n",
      "                                    190,\n",
      "                                    128,\n",
      "                                    190\n",
      "                                ],\n",
      "                                \"text\": \"On\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    156,\n",
      "                                    163,\n",
      "                                    170,\n",
      "                                    163,\n",
      "                                    171,\n",
      "                                    190,\n",
      "                                    157,\n",
      "                                    190\n",
      "                                ],\n",
      "                                \"text\": \"a\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    176,\n",
      "                                    163,\n",
      "                                    232,\n",
      "                                    163,\n",
      "                                    232,\n",
      "                                    190,\n",
      "                                    176,\n",
      "                                    190\n",
      "                                ],\n",
      "                                \"text\": \"foreign\",\n",
      "                                \"confidence\": 0.72\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    237,\n",
      "                                    163,\n",
      "                                    285,\n",
      "                                    163,\n",
      "                                    285,\n",
      "                                    189,\n",
      "                                    238,\n",
      "                                    190\n",
      "                                ],\n",
      "                                \"text\": \"lonely\",\n",
      "                                \"confidence\": 0.949\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    290,\n",
      "                                    163,\n",
      "                                    346,\n",
      "                                    163,\n",
      "                                    347,\n",
      "                                    189,\n",
      "                                    290,\n",
      "                                    189\n",
      "                                ],\n",
      "                                \"text\": \"shore?\",\n",
      "                                \"confidence\": 0.979\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            55,\n",
      "                            193,\n",
      "                            421,\n",
      "                            195,\n",
      "                            420,\n",
      "                            222,\n",
      "                            55,\n",
      "                            220\n",
      "                        ],\n",
      "                        \"text\": \"Put away your anger, your sword, your steed\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    57,\n",
      "                                    194,\n",
      "                                    85,\n",
      "                                    195,\n",
      "                                    84,\n",
      "                                    220,\n",
      "                                    55,\n",
      "                                    219\n",
      "                                ],\n",
      "                                \"text\": \"Put\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    90,\n",
      "                                    196,\n",
      "                                    132,\n",
      "                                    197,\n",
      "                                    131,\n",
      "                                    221,\n",
      "                                    89,\n",
      "                                    220\n",
      "                                ],\n",
      "                                \"text\": \"away\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    137,\n",
      "                                    198,\n",
      "                                    177,\n",
      "                                    199,\n",
      "                                    176,\n",
      "                                    222,\n",
      "                                    136,\n",
      "                                    221\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    182,\n",
      "                                    199,\n",
      "                                    230,\n",
      "                                    200,\n",
      "                                    229,\n",
      "                                    222,\n",
      "                                    181,\n",
      "                                    222\n",
      "                                ],\n",
      "                                \"text\": \"anger,\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    235,\n",
      "                                    200,\n",
      "                                    272,\n",
      "                                    200,\n",
      "                                    271,\n",
      "                                    222,\n",
      "                                    234,\n",
      "                                    222\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    277,\n",
      "                                    200,\n",
      "                                    328,\n",
      "                                    199,\n",
      "                                    327,\n",
      "                                    222,\n",
      "                                    276,\n",
      "                                    222\n",
      "                                ],\n",
      "                                \"text\": \"sword,\",\n",
      "                                \"confidence\": 0.982\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    333,\n",
      "                                    199,\n",
      "                                    372,\n",
      "                                    198,\n",
      "                                    371,\n",
      "                                    221,\n",
      "                                    332,\n",
      "                                    222\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    377,\n",
      "                                    198,\n",
      "                                    420,\n",
      "                                    196,\n",
      "                                    419,\n",
      "                                    221,\n",
      "                                    376,\n",
      "                                    221\n",
      "                                ],\n",
      "                                \"text\": \"steed\",\n",
      "                                \"confidence\": 0.986\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            121,\n",
      "                            228,\n",
      "                            353,\n",
      "                            227,\n",
      "                            353,\n",
      "                            253,\n",
      "                            121,\n",
      "                            254\n",
      "                        ],\n",
      "                        \"text\": \"And away your hatred bear\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    124,\n",
      "                                    229,\n",
      "                                    157,\n",
      "                                    229,\n",
      "                                    157,\n",
      "                                    254,\n",
      "                                    124,\n",
      "                                    253\n",
      "                                ],\n",
      "                                \"text\": \"And\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    162,\n",
      "                                    229,\n",
      "                                    206,\n",
      "                                    229,\n",
      "                                    206,\n",
      "                                    254,\n",
      "                                    162,\n",
      "                                    254\n",
      "                                ],\n",
      "                                \"text\": \"away\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    210,\n",
      "                                    229,\n",
      "                                    249,\n",
      "                                    229,\n",
      "                                    249,\n",
      "                                    254,\n",
      "                                    211,\n",
      "                                    254\n",
      "                                ],\n",
      "                                \"text\": \"your\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    254,\n",
      "                                    229,\n",
      "                                    308,\n",
      "                                    229,\n",
      "                                    309,\n",
      "                                    253,\n",
      "                                    254,\n",
      "                                    254\n",
      "                                ],\n",
      "                                \"text\": \"hatred\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    313,\n",
      "                                    229,\n",
      "                                    352,\n",
      "                                    228,\n",
      "                                    352,\n",
      "                                    252,\n",
      "                                    314,\n",
      "                                    253\n",
      "                                ],\n",
      "                                \"text\": \"bear\",\n",
      "                                \"confidence\": 0.985\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            62,\n",
      "                            255,\n",
      "                            413,\n",
      "                            257,\n",
      "                            412,\n",
      "                            286,\n",
      "                            62,\n",
      "                            284\n",
      "                        ],\n",
      "                        \"text\": \"Will you leave the sun and its shining heat\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    64,\n",
      "                                    255,\n",
      "                                    91,\n",
      "                                    256,\n",
      "                                    90,\n",
      "                                    285,\n",
      "                                    62,\n",
      "                                    285\n",
      "                                ],\n",
      "                                \"text\": \"Will\",\n",
      "                                \"confidence\": 0.986\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    97,\n",
      "                                    257,\n",
      "                                    129,\n",
      "                                    258,\n",
      "                                    128,\n",
      "                                    286,\n",
      "                                    96,\n",
      "                                    285\n",
      "                                ],\n",
      "                                \"text\": \"you\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    135,\n",
      "                                    258,\n",
      "                                    174,\n",
      "                                    259,\n",
      "                                    173,\n",
      "                                    286,\n",
      "                                    134,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"leave\",\n",
      "                                \"confidence\": 0.976\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    180,\n",
      "                                    260,\n",
      "                                    206,\n",
      "                                    260,\n",
      "                                    205,\n",
      "                                    286,\n",
      "                                    179,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"the\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    212,\n",
      "                                    260,\n",
      "                                    241,\n",
      "                                    261,\n",
      "                                    240,\n",
      "                                    286,\n",
      "                                    211,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"sun\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    247,\n",
      "                                    261,\n",
      "                                    277,\n",
      "                                    261,\n",
      "                                    276,\n",
      "                                    286,\n",
      "                                    246,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"and\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    283,\n",
      "                                    261,\n",
      "                                    304,\n",
      "                                    261,\n",
      "                                    304,\n",
      "                                    286,\n",
      "                                    282,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"its\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    310,\n",
      "                                    261,\n",
      "                                    367,\n",
      "                                    261,\n",
      "                                    367,\n",
      "                                    286,\n",
      "                                    310,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"shining\",\n",
      "                                \"confidence\": 0.963\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    373,\n",
      "                                    261,\n",
      "                                    413,\n",
      "                                    260,\n",
      "                                    412,\n",
      "                                    286,\n",
      "                                    373,\n",
      "                                    286\n",
      "                                ],\n",
      "                                \"text\": \"heat\",\n",
      "                                \"confidence\": 0.985\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            110,\n",
      "                            291,\n",
      "                            364,\n",
      "                            291,\n",
      "                            364,\n",
      "                            317,\n",
      "                            110,\n",
      "                            317\n",
      "                        ],\n",
      "                        \"text\": \"For to seek the darkness there?\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    111,\n",
      "                                    291,\n",
      "                                    136,\n",
      "                                    291,\n",
      "                                    136,\n",
      "                                    317,\n",
      "                                    110,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"For\",\n",
      "                                \"confidence\": 0.983\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    141,\n",
      "                                    292,\n",
      "                                    160,\n",
      "                                    292,\n",
      "                                    159,\n",
      "                                    317,\n",
      "                                    141,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"to\",\n",
      "                                \"confidence\": 0.988\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    165,\n",
      "                                    292,\n",
      "                                    199,\n",
      "                                    292,\n",
      "                                    198,\n",
      "                                    317,\n",
      "                                    164,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"seek\",\n",
      "                                \"confidence\": 0.985\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    204,\n",
      "                                    292,\n",
      "                                    231,\n",
      "                                    292,\n",
      "                                    231,\n",
      "                                    317,\n",
      "                                    203,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"the\",\n",
      "                                \"confidence\": 0.987\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    236,\n",
      "                                    292,\n",
      "                                    306,\n",
      "                                    292,\n",
      "                                    305,\n",
      "                                    317,\n",
      "                                    236,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"darkness\",\n",
      "                                \"confidence\": 0.981\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    311,\n",
      "                                    292,\n",
      "                                    365,\n",
      "                                    292,\n",
      "                                    365,\n",
      "                                    317,\n",
      "                                    310,\n",
      "                                    317\n",
      "                                ],\n",
      "                                \"text\": \"there?\",\n",
      "                                \"confidence\": 0.952\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            117,\n",
      "                            338,\n",
      "                            215,\n",
      "                            338,\n",
      "                            215,\n",
      "                            348,\n",
      "                            117,\n",
      "                            348\n",
      "                        ],\n",
      "                        \"text\": \"///Licensed under che SI\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    117,\n",
      "                                    339,\n",
      "                                    160,\n",
      "                                    339,\n",
      "                                    160,\n",
      "                                    348,\n",
      "                                    117,\n",
      "                                    348\n",
      "                                ],\n",
      "                                \"text\": \"///Licensed\",\n",
      "                                \"confidence\": 0.562\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    162,\n",
      "                                    339,\n",
      "                                    183,\n",
      "                                    339,\n",
      "                                    183,\n",
      "                                    348,\n",
      "                                    162,\n",
      "                                    348\n",
      "                                ],\n",
      "                                \"text\": \"under\",\n",
      "                                \"confidence\": 0.638\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    185,\n",
      "                                    339,\n",
      "                                    198,\n",
      "                                    339,\n",
      "                                    198,\n",
      "                                    348,\n",
      "                                    185,\n",
      "                                    348\n",
      "                                ],\n",
      "                                \"text\": \"che\",\n",
      "                                \"confidence\": 0.782\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    200,\n",
      "                                    339,\n",
      "                                    214,\n",
      "                                    339,\n",
      "                                    214,\n",
      "                                    348,\n",
      "                                    200,\n",
      "                                    348\n",
      "                                ],\n",
      "                                \"text\": \"SI\",\n",
      "                                \"confidence\": 0.638\n",
      "                            }\n",
      "                        ]\n",
      "                    }\n",
      "                ]\n",
      "            }\n",
      "        ]\n",
      "    }\n",
      "}\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#提取文本\n",
    "import json\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "text_recognition_url = endpoint + \"/vision/v3.0/read/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to recognize.\n",
    "image_url = \"https://tse3-mm.cn.bing.net/th/id/OIP.JXXxM60tJCEUiNT6QBXPvAHaFj?pid=Api&rs=1\"\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"15f98ea70f74425382a7f6b768cd9915\"}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    \n",
    "    print(json.dumps(analysis, indent=4))\n",
    "\n",
    "    time.sleep(1)\n",
    "    if (\"analyzeResult\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"analyzeResult\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"analyzeResult\"][\"readResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#提取文本\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "ocr_url = endpoint + \"vision/v3.1/ocr\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://tse3-mm.cn.bing.net/th/id/OIP.JXXxM60tJCEUiNT6QBXPvAHaFj?pid=Api&rs=1\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"15f98ea70f74425382a7f6b768cd9915\"}\n",
    "params = {'language': 'unk', 'detectOrientation': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(ocr_url, headers=headers, params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "analysis = response.json()\n",
    "\n",
    "# Extract the word bounding boxes and text.\n",
    "line_infos = [region[\"lines\"] for region in analysis[\"regions\"]]\n",
    "word_infos = []\n",
    "for line in line_infos:\n",
    "    for word_metadata in line:\n",
    "        for word_info in word_metadata[\"words\"]:\n",
    "            word_infos.append(word_info)\n",
    "word_infos\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "plt.figure(figsize=(5, 5))\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image, alpha=0.5)\n",
    "for word in word_infos:\n",
    "    bbox = [int(num) for num in word[\"boundingBox\"].split(\",\")]\n",
    "    text = word[\"text\"]\n",
    "    origin = (bbox[0], bbox[1])\n",
    "    patch = Rectangle(origin, bbox[2], bbox[3],\n",
    "                      fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(origin[0], origin[1], text, fontsize=20, weight=\"bold\", va=\"top\")\n",
    "plt.show()\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习人脸识别和计算机视觉心得体会\n",
    "\n",
    "> 历程艰辛！    \n",
    "> 不会放弃！    \n",
    "> 终得成果！    \n",
    "> 分享经验吧～"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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